Udacity - Deep Learning Foundation v1 0 0 Torrent Downloads

Part 02-Module 03-Lesson 01_MiniFlow

07. Pixels are Features!-qE5YYXtPq9U.en-US.vtt

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07. Pixels are Features!-qE5YYXtPq9U.pt-BR.vtt

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01. Miniflow Introduction-Nqp_UifEwt0.zh-CN.vtt

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cost.png

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gradient-descent-divergence.gif

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gradient-descent-convergence.gif

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neuron.png

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w1-backprop-graph.png

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addition-graph.png

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topological-sort.001.jpeg

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example-neural-network.png

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screen-shot-2016-10-26-at-19.28.34.png

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boston-back-bay-reflection.jpg

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screen-shot-2016-10-21-at-15.43.05.png

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01. Miniflow Introduction-Nqp_UifEwt0.en-US.vtt

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01. Miniflow Introduction-Nqp_UifEwt0.pt-BR.vtt

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index.html

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01. Welcome to MiniFlow.html

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15. Outro.html

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10. Cost Solution.html

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02. Graphs.html

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03. MiniFlow Architecture.html

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11. Gradient Descent.html

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05. Forward Propagation Solution.html

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06. Learning and Loss.html

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04. Forward Propagation.html

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08. Sigmoid Function.html

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09. Cost.html

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07. Linear Transform.html

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14. SGD Solution.html

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13. Stochastic Gradient Descent.html

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12. Backpropagation.html

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media

input-to-output-2.mp4

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07. Pixels are Features!-qE5YYXtPq9U.mp4

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01. Miniflow Introduction-Nqp_UifEwt0.mp4

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Part 02-Module 05-Lesson 02_Convolutional Networks

01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt

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01. Intro to CNNs-B61jxZ4rkMs.en.vtt

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01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt

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01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt

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01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt

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07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt

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07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt

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02. Color-Question-BdQccpMwk80.zh-CN.vtt

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07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt

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02. Color-Question-BdQccpMwk80.pt-BR.vtt

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02. Color-Question-BdQccpMwk80.en.vtt

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08. Convolutions Cont.-utOv-BKI_vo.zh-CN.vtt

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08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt

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07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.zh-CN.vtt

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08. Convolutions Cont.-utOv-BKI_vo.en.vtt

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07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.en.vtt

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07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.pt-BR.vtt

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29. Inception Module-SlTm03bEOxA.zh-CN.vtt

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28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt

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29. Inception Module-SlTm03bEOxA.pt-BR.vtt

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29. Inception Module-SlTm03bEOxA.en.vtt

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28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt

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28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt

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03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt

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03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt

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03. Statistical Invariance-0Hr5YwUUhr0.en.vtt

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18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt

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18. Explore the Design Space-FG7M9tWH2nQ.en.vtt

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18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt

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04. Convolutional Networks-ISHGyvsT0QY.zh-CN.vtt

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04. Convolutional Networks-ISHGyvsT0QY.en.vtt

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04. Convolutional Networks-ISHGyvsT0QY.pt-BR.vtt

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img

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max-pooling.png

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maxpool.jpeg

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layer-1-grid.png

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heirarchy-diagram.jpg

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convolution-schematic.gif

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filter-depth.png

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dog-1210559-1280.jpg

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vlcsnap-2016-11-24-15h52m47s438.png

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teeth-whiskers-tongue.png

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vlcsnap-2016-11-24-16h01m35s262.png

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retriever-patch.png

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retriever-patch-shifted.png

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convolutionalnetworksquiz.png

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arch.png

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screen-shot-2016-11-24-at-12.09.24-pm.png

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index.html

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25. Solution Pooling Practice.html

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27. Solution Average Pooling.html

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28. 1x1 Convolutions.html

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29. Inception Module.html

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13. Solution Number of Parameters.html

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04. Convolutional Networks.html

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03. Statistical Invariance.html

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18. Explore The Design Space.html

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15. Solution Parameter Sharing.html

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01. Intro To CNNs.html

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23. Solution Pooling Mechanics.html

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08. Convolutions continued.html

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21. Solution Pooling Intuition.html

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26. Quiz Average Pooling.html

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35. CNNs - Additional Resources.html

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02. Color.html

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24. Quiz Pooling Practice.html

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20. Quiz Pooling Intuition.html

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34. Solution TensorFlow Pooling Layer.html

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12. Quiz Number of Parameters.html

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10. Quiz Convolution Output Shape.html

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14. Quiz Parameter Sharing.html

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22. Quiz Pooling Mechanics.html

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32. Solution TensorFlow Convolution Layer.html

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11. Solution Convolution Output Shape.html

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33. TensorFlow Pooling Layer.html

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17. TensorFlow Convolution Layer.html

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19. TensorFlow Max Pooling.html

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07. Feature Map Sizes.html

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31. TensorFlow Convolution Layer.html

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09. Parameters.html

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05. Intuition.html

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06. Filters.html

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16. Visualizing CNNs.html

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30. Convolutional Network in TensorFlow.html

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07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4

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02. Color-Question-BdQccpMwk80.mp4

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07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4

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01. Intro to CNNs-B61jxZ4rkMs.mp4

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08. Convolutions Cont.-utOv-BKI_vo.mp4

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03. Statistical Invariance-0Hr5YwUUhr0.mp4

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29. Inception Module-SlTm03bEOxA.mp4

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28. 1x1 Convolutions-Zmzgerm6SjA.mp4

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18. Explore the Design Space-FG7M9tWH2nQ.mp4

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04. Convolutional Networks-ISHGyvsT0QY.mp4

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Part 07-Module 01-Lesson 01_Introduction to Neural Networks

10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt

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10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt

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10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt

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15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt

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16. Quiz - Softmax-NNoezNnAMTY.en.vtt

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16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt

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21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt

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16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt

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15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt

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15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt

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30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt

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21. Formula For Cross 1-qvr_ego_d6w.en.vtt

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30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt

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30. Non-Linear Data-F7ZiE8PQiSc.en.vtt

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21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt

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13. Error Functions-YfUUunxWIJw.zh-CN.vtt

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13. Error Functions-YfUUunxWIJw.en.vtt

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13. Error Functions-YfUUunxWIJw.pt-BR.vtt

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19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt

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19. Quiz - Cross 1--xxrisIvD0E.en.vtt

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img

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sigmoid-derivative.gif

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codecogseqn-49.gif

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codecogseqn-43.gif

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codecogseqn-60-2.png

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points.png

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perceptronquiz.png

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xor-quiz.png

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meme.png

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xor.png

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and-quiz.png

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or-quiz.png

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and-to-or.png

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student-quiz.png

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19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt

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09. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt

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09. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt

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09. XOR Perceptron-TF83GfjYLdw.en.vtt

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31. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt

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29. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt

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08. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt

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08. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt

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31. Non-Linear Models-HWuBKCZsCo8.en.vtt

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29. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt

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29. Continuous Perceptrons-07-JJ-aGEfM.en.vtt

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08. Why Neural Networks-zAkzOZntK6Y.en.vtt

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31. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt

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34. Chain Rule-YAhIBOnbt54.zh-CN.vtt

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23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt

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18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt

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23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt

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12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt

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12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt

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04. Classification Example-46PywnGa_cQ.pt-BR.vtt

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18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt

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23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt

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18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt

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04. Classification Example-46PywnGa_cQ.zh-CN.vtt

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34. Chain Rule-YAhIBOnbt54.en.vtt

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33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt

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34. Chain Rule-YAhIBOnbt54.pt-BR.vtt

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04. Classification Example-46PywnGa_cQ.en.vtt

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12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt

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32. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt

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33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt

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17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt

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17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt

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19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt

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32. Multiclass Classification-uNTtvxwfox0.en.vtt

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32. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt

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33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt

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25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt

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17. One-Hot Encoding-AePvjhyvsBo.en.vtt

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19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt

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19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt

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16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt

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10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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03. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

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10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

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09. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

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03. Classsification Example-Dh625piH7Z0.pt-BR.vtt

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16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt

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25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt

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16. DL 18 S Softmax-n8S-v_LCTms.en.vtt

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25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt

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10. Perceptron Algorithm--zhTROHtscQ.en.vtt

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06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt

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03. Classsification Example-Dh625piH7Z0.en.vtt

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32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt

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02. Introduction-tn-CrUTkCUc.zh-CN.vtt

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34. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt

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06. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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09. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt

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32. Layers-pg99FkXYK0M.zh-CN.vtt

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02. Introduction-tn-CrUTkCUc.pt-BR.vtt

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09. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

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11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

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02. Introduction-tn-CrUTkCUc.en.vtt

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32. Layers-pg99FkXYK0M.pt-BR.vtt

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32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt

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05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

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32. Layers-pg99FkXYK0M.en.vtt

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34. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt

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34. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt

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11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

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10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

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28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt

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05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt

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18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt

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24. Gradient Descent-rhVIF-nigrY.en.vtt

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05. Linear Boundaries-X-uMlsBi07k.en.vtt

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24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt

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22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt

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20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt

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10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt

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23. Error Function-V5kkHldUlVU.zh-CN.vtt

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10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt

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28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt

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28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt

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16. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt

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18. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt

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18. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt

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22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt

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32. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt

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15. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt

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22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt

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20. Cross Entropy 1-iREoPUrpXvE.en.vtt

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23. Error Function-V5kkHldUlVU.en.vtt

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07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt

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20. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt

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34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt

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16. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt

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23. Error Function-V5kkHldUlVU.pt-BR.vtt

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32. Combinando modelos-Boy3zHVrWB4.en.vtt

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32. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt

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33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt

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16. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt

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07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt

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15. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt

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15. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt

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07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt

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34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt

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33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt

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index.html

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34. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt

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34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt

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21. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt

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33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt

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34. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt

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34. Backpropagation V2-1SmY3TZTyUk.en.vtt

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21. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt

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21. CrossEntropy V1-1BnhC6e0TFw.en.vtt

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14. Error Functions-jfKShxGAbok.zh-CN.vtt

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02. Introduction.html

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37. Outro.html

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30. Non-linear Data.html

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13. Error Functions.html

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17. One-Hot Encoding.html

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31. Non-Linear Models.html

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12. Non-Linear Regions.html

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08. Why Neural Networks.html

8.42 KB

29. Continuous Perceptrons.html

8.43 KB

04. Classification Problems 2.html

8.43 KB

25. Logistic Regression Algorithm.html

8.46 KB

01. Instructor.html

8.52 KB

20. Cross-Entropy 1.html

8.63 KB

28. Perceptron vs Gradient Descent.html

8.77 KB

27. Notebook Gradient Descent.html

8.82 KB

36. Notebook Analyzing Student Data.html

8.83 KB

05. Linear Boundaries.html

9.13 KB

14. Error Functions-jfKShxGAbok.pt-BR.vtt

9.14 KB

35. Pre-Lab Analyzing Student Data.html

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07. Perceptrons.html

9.44 KB

22. Multi-Class Cross Entropy.html

9.44 KB

14. Error Functions-jfKShxGAbok.en.vtt

9.45 KB

06. Higher Dimensions.html

9.49 KB

03. Classification Problems 1.html

9.51 KB

33. Feedforward.html

9.67 KB

14. Log-loss Error Function.html

9.84 KB

23. Logistic Regression.html

10.07 KB

26. Pre-Lab Gradient Descent.html

10.14 KB

19. Maximizing Probabilities.html

10.14 KB

15. Discrete vs Continuous.html

10.52 KB

18. Maximum Likelihood.html

10.59 KB

21. Cross-Entropy 2.html

10.97 KB

10. Perceptron Trick.html

12.11 KB

34. Backpropagation.html

12.49 KB

16. Softmax.html

12.63 KB

32. Neural Network Architecture.html

12.92 KB

11. Perceptron Algorithm.html

15.85 KB

24. Gradient Descent.html

16.13 KB

09. Perceptrons as Logical Operators.html

19.07 KB

10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4

260.01 KB

09. XOR Perceptron-TF83GfjYLdw.mp4

947.00 KB

08. Why Neural Networks-zAkzOZntK6Y.mp4

982.27 KB

31. Non-Linear Models-HWuBKCZsCo8.mp4

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29. Continuous Perceptrons-07-JJ-aGEfM.mp4

1.13 MB

12. Non-Linear Regions-B8UrWnHh1Wc.mp4

1.33 MB

34. Chain Rule-YAhIBOnbt54.mp4

1.46 MB

23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4

1.49 MB

04. Classification Example-46PywnGa_cQ.mp4

1.62 MB

17. One-Hot Encoding-AePvjhyvsBo.mp4

1.65 MB

33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4

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16. Quiz - Softmax-NNoezNnAMTY.mp4

1.73 MB

19. Quiz Cross Entropy-njq6bYrPqSU.mp4

1.86 MB

32. Multiclass Classification-uNTtvxwfox0.mp4

1.88 MB

10. Perceptron Algorithm--zhTROHtscQ.mp4

1.92 MB

16. DL 18 S Softmax-n8S-v_LCTms.mp4

1.95 MB

25. Gradient Descent Algorithm-snxmBgi_GeU.mp4

1.98 MB

03. Classsification Example-Dh625piH7Z0.mp4

2.07 MB

21. Formula For Cross 1-qvr_ego_d6w.mp4

2.08 MB

30. Non-Linear Data-F7ZiE8PQiSc.mp4

2.14 MB

15. Discrete vs Continuous-rdP-RPDFkl0.mp4

2.26 MB

06. 09 Higher Dimensions-eBHunImDmWw.mp4

2.59 MB

09. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.68 MB

32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4

2.83 MB

11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.87 MB

19. Quiz - Cross 1--xxrisIvD0E.mp4

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32. Layers-pg99FkXYK0M.mp4

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28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4

3.20 MB

34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4

3.31 MB

13. Error Functions-YfUUunxWIJw.mp4

3.54 MB

10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.66 MB

24. Gradient Descent-rhVIF-nigrY.mp4

3.76 MB

18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4

3.85 MB

05. Linear Boundaries-X-uMlsBi07k.mp4

3.85 MB

16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4

4.01 MB

22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4

4.14 MB

20. Cross Entropy 1-iREoPUrpXvE.mp4

4.22 MB

32. Combinando modelos-Boy3zHVrWB4.mp4

4.73 MB

23. Error Function-V5kkHldUlVU.mp4

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07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

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33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4

5.33 MB

15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4

5.35 MB

34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4

5.69 MB

18. Maximum Likelihood 1-1yJx-QtlvNI.mp4

5.75 MB

34. Backpropagation V2-1SmY3TZTyUk.mp4

6.52 MB

21. CrossEntropy V1-1BnhC6e0TFw.mp4

6.61 MB

14. Error Functions-jfKShxGAbok.mp4

7.21 MB

02. Introduction-tn-CrUTkCUc.mp4

7.54 MB

Part 07-Module 01-Lesson 03_Training Neural Networks

10. Random Restart-idyBBCzXiqg.zh-CN.vtt

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10. Random Restart-idyBBCzXiqg.en.vtt

0.46 KB

10. Random Restart-idyBBCzXiqg.pt-BR.vtt

0.47 KB

02. Training Optimization-UiGKhx9pUYc.en.vtt

0.80 KB

02. Training Optimization-UiGKhx9pUYc.zh-CN.vtt

0.82 KB

02. Training Optimization-UiGKhx9pUYc.pt-BR.vtt

0.85 KB

14. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt

1.00 KB

09. Local Minima-gF_sW_nY-xw.zh-CN.vtt

1.01 KB

06. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt

1.02 KB

09. Local Minima-gF_sW_nY-xw.pt-BR.vtt

1.05 KB

16. Error Functions Around the World-34AAcTECu2A.zh-CN.vtt

1.06 KB

16. Error Functions Around the World-34AAcTECu2A.pt-BR.vtt

1.08 KB

14. Learning Rate-TwJ8aSZoh2U.en.vtt

1.12 KB

09. Local Minima-gF_sW_nY-xw.en.vtt

1.14 KB

06. DL 53 Q Regularization-KxROxcRsHL8.en.vtt

1.15 KB

06. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt

1.16 KB

16. Error Functions Around the World-34AAcTECu2A.en.vtt

1.17 KB

11. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt

1.24 KB

14. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt

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11. Vanishing Gradient-W_JJm_5syFw.en.vtt

1.46 KB

11. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt

1.56 KB

03. Testing-EeBZpb-PSac.zh-CN.vtt

1.99 KB

15. Momentum-r-rYz_PEWC8.zh-CN.vtt

2.21 KB

12. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt

2.34 KB

03. Testing-EeBZpb-PSac.pt-BR.vtt

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03. Testing-EeBZpb-PSac.en.vtt

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15. Momentum-r-rYz_PEWC8.en.vtt

2.50 KB

12. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt

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12. Other Activation Functions-kA-1vUt6cvQ.en.vtt

2.68 KB

15. Momentum-r-rYz_PEWC8.pt-BR.vtt

2.70 KB

08. Dropout-Ty6K6YiGdBs.zh-CN.vtt

4.06 KB

13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt

4.10 KB

index.html

4.61 KB

13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt

4.63 KB

13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt

4.64 KB

05. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt

4.65 KB

08. Dropout-Ty6K6YiGdBs.pt-BR.vtt

4.66 KB

08. Dropout-Ty6K6YiGdBs.en.vtt

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05. Model Complexity Graph-NnS0FJyVcDQ.en.vtt

5.32 KB

05. Model Complexity Graph-NnS0FJyVcDQ.pt-BR.vtt

5.52 KB

08. Dropout.html

6.09 KB

03. Testing.html

6.09 KB

15. Momentum.html

6.10 KB

09. Local Minima.html

6.13 KB

10. Random Restart.html

6.14 KB

07. Regularization 2.html

6.15 KB

14. Learning Rate Decay.html

6.15 KB

11. Vanishing Gradient.html

6.17 KB

01. Instructor.html

6.18 KB

05. Early Stopping.html

6.18 KB

02. Training Optimization.html

6.19 KB

04. Overfitting and Underfitting.html

6.24 KB

16. Error Functions Around the World.html

6.27 KB

13. Batch vs Stochastic Gradient Descent.html

6.29 KB

12. Other Activation Functions.html

6.53 KB

04. Underfitting And Overfitting-xj4PlXMsN-Y.zh-CN.vtt

6.54 KB

07. Regularization-ndYnUrx8xvs.zh-CN.vtt

6.96 KB

06. Regularization.html

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04. Underfitting And Overfitting-xj4PlXMsN-Y.en.vtt

7.49 KB

07. Regularization-ndYnUrx8xvs.en.vtt

8.07 KB

04. Underfitting And Overfitting-xj4PlXMsN-Y.pt-BR.vtt

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07. Regularization-ndYnUrx8xvs.pt-BR.vtt

8.78 KB

img

regularization-quiz.png

87.90 KB

screen-shot-2018-03-19-at-3.49.28-pm.png

471.61 KB

10. Random Restart-idyBBCzXiqg.mp4

394.99 KB

09. Local Minima-gF_sW_nY-xw.mp4

819.86 KB

14. Learning Rate-TwJ8aSZoh2U.mp4

927.05 KB

06. DL 53 Q Regularization-KxROxcRsHL8.mp4

1.01 MB

11. Vanishing Gradient-W_JJm_5syFw.mp4

1.32 MB

16. Error Functions Around the World-34AAcTECu2A.mp4

1.73 MB

03. Testing-EeBZpb-PSac.mp4

2.00 MB

15. Momentum-r-rYz_PEWC8.mp4

2.14 MB

12. Other Activation Functions-kA-1vUt6cvQ.mp4

2.30 MB

02. Training Optimization-UiGKhx9pUYc.mp4

2.96 MB

13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4

3.95 MB

08. Dropout-Ty6K6YiGdBs.mp4

4.22 MB

05. Model Complexity Graph-NnS0FJyVcDQ.mp4

4.90 MB

04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4

6.42 MB

07. Regularization-ndYnUrx8xvs.mp4

7.57 MB

Part 08-Module 01-Lesson 07_Deep Learning for Cancer Detection with Sebastian Thrun

13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt

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16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt

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07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt

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16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt

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28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt

0.46 KB

07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt

0.47 KB

13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt

0.48 KB

13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt

0.49 KB

16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt

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28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt

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07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt

0.51 KB

28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt

0.53 KB

20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt

0.63 KB

19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt

0.66 KB

19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt

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08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt

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10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt

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10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt

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14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt

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14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt

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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt

0.77 KB

08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt

0.79 KB

01. Introduction-ZCpXvVdIdnY.zh-CN.vtt

0.80 KB

20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt

0.80 KB

14. Solution Sensitivty And Specificity-GBZjyeMjKxc.en.vtt

0.83 KB

10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt

0.83 KB

01. Introduction-ZCpXvVdIdnY.pt-BR.vtt

0.84 KB

08. Solution Data Challenges-1z3o4niQuNg.en.vtt

0.85 KB

01. Introduction-ZCpXvVdIdnY.en.vtt

0.85 KB

06. 06 Image Challenge V3-Efnoj1KNPHw.zh-CN.vtt

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06. 06 Image Challenge V3-Efnoj1KNPHw.pt-BR.vtt

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20. Solution ROC Curve-sdUUf6RRmXI.en.vtt

0.92 KB

11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.zh-CN.vtt

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09. Training The Neural Network-HwiI-UXUx-M.pt-BR.vtt

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09. Training The Neural Network-HwiI-UXUx-M.zh-CN.vtt

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03. Survival Rate-QPlp3NeGuSk.zh-CN.vtt

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11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt

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06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt

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03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt

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09. Training The Neural Network-HwiI-UXUx-M.en.vtt

1.11 KB

11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.en.vtt

1.14 KB

04. Medical Classification-RCOSP60dV7U.zh-CN.vtt

1.17 KB

04. Medical Classification-RCOSP60dV7U.pt-BR.vtt

1.18 KB

03. Survival Rate-QPlp3NeGuSk.en.vtt

1.19 KB

17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.zh-CN.vtt

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17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt

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04. Medical Classification-RCOSP60dV7U.en.vtt

1.37 KB

25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt

1.41 KB

17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt

1.47 KB

22. Visualization-aGIGB4Ta3_A.zh-CN.vtt

1.48 KB

25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt

1.54 KB

05. The Data-2RLbbV7MQNA.zh-CN.vtt

1.64 KB

25. Confusion Matrix-3rpN-YYlfes.en.vtt

1.66 KB

02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt

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05. The Data-2RLbbV7MQNA.pt-BR.vtt

1.71 KB

23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt

1.72 KB

02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt

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22. Visualization-aGIGB4Ta3_A.pt-BR.vtt

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22. Visualization-aGIGB4Ta3_A.en.vtt

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02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt

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23. What Is The Neural Network Looking At-qN-rvoxPbBw.zh-CN.vtt

1.87 KB

05. The Data-2RLbbV7MQNA.en.vtt

1.97 KB

23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt

2.17 KB

12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt

2.28 KB

12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt

2.31 KB

21. ROC Curve-fWwe_JlpnlQ.zh-CN.vtt

2.58 KB

21. ROC Curve-fWwe_JlpnlQ.pt-BR.vtt

2.64 KB

12. Validating The Training-Oxm9ofvov3I.en.vtt

2.65 KB

21. ROC Curve-fWwe_JlpnlQ.en.vtt

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26. Conclusion-WhpE_8sTt-0.zh-CN.vtt

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26. Conclusion-WhpE_8sTt-0.pt-BR.vtt

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26. Conclusion-WhpE_8sTt-0.en.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt

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index.html

5.97 KB

18. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt

7.30 KB

05. The data.html

7.75 KB

26. Conclusion.html

7.77 KB

01. Intro.html

7.77 KB

22. Visualization.html

7.79 KB

25. Confusion Matrix.html

7.81 KB

21. Comparing our Results with Doctors.html

7.81 KB

20. Solution ROC Curve.html

7.82 KB

03. Survival Probability of Skin Cancer.html

7.83 KB

04. Medical Classification.html

7.85 KB

12. Validating the Training.html

7.86 KB

08. Solution Data Challenges.html

7.86 KB

28. Mini Project Introduction.html

7.87 KB

09. Training the Neural Network.html

7.88 KB

23. What is the network looking at.html

7.94 KB

14. Solution Sensitivity and Specificity.html

7.94 KB

11. Solution Random vs Pre-initialized Weight.html

7.99 KB

18. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt

8.12 KB

06. Image Challenges.html

8.34 KB

18. ROC Curve-2Iw5TiGzJI4.en.vtt

8.66 KB

16. Quiz Diagnosing Cancer.html

8.73 KB

07. Quiz Data Challenges.html

8.83 KB

10. Quiz Random vs Pre-initialized Weights.html

8.86 KB

19. Quiz ROC Curve.html

8.92 KB

27. Useful Resources.html

8.96 KB

13. Quiz Sensitivity and Specificity.html

9.38 KB

02. Skin Cancer.html

9.66 KB

17. Solution Diagnosing Cancer.html

10.23 KB

15. More on Sensitivity and Specificity.html

10.85 KB

18. Refresh on ROC Curves.html

11.04 KB

24. Refresh on Confusion Matrices.html

14.72 KB

29. Mini Project Dermatologist AI.html

19.52 KB

img

roc-curve.png

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roc.png

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sample-confusion-matrix.png

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roc-curves.png

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sensitivity-specificity.png

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cat-1.jpeg

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cat-2.jpeg

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confusion-matrix.png

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threshold.png

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cat-3.png

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nature.png

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lesions.png

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skin-disease-classes.png

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media

monkey-doctor.png

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18. Images-1GdiN5Wc8LA.mp4

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07. 07 Quiz Data Challenges V1-F8yc7BlV93c.mp4

862.50 KB

13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4

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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4

1.11 MB

20. Solution ROC Curve-sdUUf6RRmXI.mp4

1.11 MB

16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4

1.14 MB

28. Mini Project Introduction-Rgf3YVFWl-M.mp4

1.15 MB

09. Training The Neural Network-HwiI-UXUx-M.mp4

1.24 MB

06. 06 Image Challenge V3-Efnoj1KNPHw.mp4

1.46 MB

08. Solution Data Challenges-1z3o4niQuNg.mp4

1.49 MB

03. Survival Rate-QPlp3NeGuSk.mp4

1.52 MB

01. Introduction-ZCpXvVdIdnY.mp4

1.55 MB

14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4

1.57 MB

10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4

1.58 MB

04. Medical Classification-RCOSP60dV7U.mp4

2.20 MB

17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4

2.31 MB

22. Visualization-aGIGB4Ta3_A.mp4

2.43 MB

21. ROC Curve-fWwe_JlpnlQ.mp4

2.68 MB

11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4

2.83 MB

05. The Data-2RLbbV7MQNA.mp4

2.85 MB

25. Confusion Matrix-3rpN-YYlfes.mp4

3.07 MB

23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4

3.46 MB

02. 02 Skin Cancer V4-70jGZeiTNgk.mp4

4.73 MB

24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4

5.04 MB

12. Validating The Training-Oxm9ofvov3I.mp4

5.51 MB

18. ROC Curve-2Iw5TiGzJI4.mp4

6.66 MB

26. Conclusion-WhpE_8sTt-0.mp4

8.20 MB

README.txt

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Part 01-Module 03-Lesson 02_Intro to Neural Networks

01. Introducing Luis-nto-stLuN6M.zh-CN.vtt

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01. Introducing Luis-nto-stLuN6M.pt-BR.vtt

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01. Introducing Luis-nto-stLuN6M.en-US.vtt

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14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt

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10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt

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10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt

1.52 KB

10. Gradient Descent-29PmNG7fuuM.en.vtt

1.60 KB

14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt

1.65 KB

img

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hidden-errors.gif

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hq-perceptron.png

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a-b-c-fill-nn.png

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14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt

1.71 KB

02. Logistic Regression - Question-kSs6O3R7JUI.zh-CN.vtt

1.82 KB

02. Logistic Regression - Question-kSs6O3R7JUI.pt-BR.vtt

2.01 KB

15. Backpropagation-MZL97-2joxQ.zh-CN.vtt

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02. Logistic Regression - Question-kSs6O3R7JUI.en-US.vtt

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15. Backpropagation-MZL97-2joxQ.pt-BR.vtt

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15. Backpropagation-MZL97-2joxQ.en-US.vtt

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index.html

4.80 KB

03. Logistic Regression - Solution-1iNylA3fJDs.zh-CN.vtt

5.20 KB

03. Logistic Regression - Solution-1iNylA3fJDs.en-US.vtt

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03. Logistic Regression - Solution-1iNylA3fJDs.pt-BR.vtt

5.99 KB

04. Neural Networks.html

6.36 KB

01. Introducing Luis.html

6.38 KB

03. Logistic Regression Answer.html

6.46 KB

17. Further Reading.html

6.57 KB

11. Gradient Descent The Math.html

6.74 KB

02. Logistic Regression Quiz.html

7.29 KB

04. Neural Networks-Mqogpnp1lrU.zh-CN.vtt

9.06 KB

11. Gradient Descent-Math-7sxA5Ap8AWM.zh-CN.vtt

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04. Neural Networks-Mqogpnp1lrU.pt-BR.vtt

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08. XOR Perceptron Quiz.html

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04. Neural Networks-Mqogpnp1lrU.en.vtt

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06. AND Perceptron Quiz.html

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07. OR NOT Perceptron Quiz.html

10.63 KB

11. Gradient Descent-Math-7sxA5Ap8AWM.en.vtt

10.81 KB

11. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt

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12. Gradient Descent The Code.html

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09. The Simplest Neural Network.html

13.39 KB

10. Gradient Descent.html

13.44 KB

15. Backpropagation.html

20.18 KB

05. Perceptron.html

21.63 KB

14. Multilayer Perceptrons.html

21.80 KB

16. Implementing Backpropagation.html

22.24 KB

13. Implementing Gradient Descent.html

27.47 KB

10. Gradient Descent-29PmNG7fuuM.mp4

2.46 MB

14. Multilayer perceptrons-Rs9petvTBLk.mp4

2.85 MB

15. Backpropagation-MZL97-2joxQ.mp4

3.44 MB

02. Logistic Regression - Question-kSs6O3R7JUI.mp4

3.45 MB

01. Introducing Luis-nto-stLuN6M.mp4

3.63 MB

03. Logistic Regression - Solution-1iNylA3fJDs.mp4

7.36 MB

11. Gradient Descent-Math-7sxA5Ap8AWM.mp4

11.25 MB

04. Neural Networks-Mqogpnp1lrU.mp4

14.92 MB

Part 07-Module 01-Lesson 05_Keras

06. Keras Lab-a50un22BsLI.zh-CN.vtt

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06. Keras Lab-a50un22BsLI.pt-BR.vtt

0.56 KB

06. Keras Lab-a50un22BsLI.en.vtt

0.57 KB

index.html

4.02 KB

06. Mini Project Intro.html

5.36 KB

08. Lab IMDB Data in Keras.html

5.82 KB

04. Lab Student Admissions in Keras.html

5.84 KB

01. Intro.html

6.05 KB

05. Optimizers in Keras.html

6.17 KB

07. Pre-Lab IMDB Data in Keras.html

9.58 KB

03. Pre-Lab Student Admissions in Keras.html

11.51 KB

02. Keras.html

15.58 KB

img

student-acceptance.png

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data.png

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summary.png

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06. Keras Lab-a50un22BsLI.mp4

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Part 02-Module 05-Lesson 01_Deep Neural Networks

10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt

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10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt

0.63 KB

12. Dropout Pt. 2-8nG8zzJMbZw.pt-BR.vtt

1.05 KB

12. Dropout Pt. 2-8nG8zzJMbZw.zh-CN.vtt

1.10 KB

12. Dropout Pt. 2-8nG8zzJMbZw.en-US.vtt

1.23 KB

09. Regularization-QcJBhbuCl5g.zh-CN.vtt

1.27 KB

08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt

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08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt

1.32 KB

01. Mat HS-9P7UPWFu8w8.zh-CN.vtt

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08. Regularization Intro-pECnr-5F3_Q.en.vtt

1.44 KB

08. Regularization Intro-pECnr-5F3_Q.en-US.vtt

1.45 KB

09. Regularization-QcJBhbuCl5g.en.vtt

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09. Regularization-QcJBhbuCl5g.pt-BR.vtt

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01. Mat HS-9P7UPWFu8w8.en-US.vtt

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08. Regularization Intro-pECnr-5F3_Q.ja-JP.vtt

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05. Training a Deep Learning Network-CsB7yUtMJyk.zh-CN.vtt

1.90 KB

05. Training a Deep Learning Network-CsB7yUtMJyk.pt-BR.vtt

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05. Training a Deep Learning Network-CsB7yUtMJyk.en.vtt

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11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt

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11. Dropout RENDER-6DcImJS8uV8.pt-BR.vtt

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index.html

4.48 KB

01. Intro to Deep Neural Networks.html

5.83 KB

11. Dropout.html

5.94 KB

09. Regularization.html

5.94 KB

12. Dropout Pt. 2.html

5.94 KB

05. Training a Deep Learning Network.html

6.06 KB

08. Regularization Intro.html

6.23 KB

02. Two-Layer Neural Network.html

6.41 KB

10. Regularization Quiz.html

6.94 KB

03. Quiz TensorFlow ReLUs.html

9.41 KB

07. Finetuning.html

10.08 KB

04. Deep Neural Network in TensorFlow.html

11.57 KB

13. Quiz TensorFlow Dropout.html

13.56 KB

06. Save and Restore TensorFlow Models.html

14.68 KB

img

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10. Regularization-Quiz-E0eEW6V0_sA.mp4

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12. Dropout Pt. 2-8nG8zzJMbZw.mp4

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09. Regularization-QcJBhbuCl5g.mp4

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05. Training a Deep Learning Network-CsB7yUtMJyk.mp4

2.56 MB

11. Dropout RENDER-6DcImJS8uV8.mp4

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08. Regularization Intro-pECnr-5F3_Q.mp4

8.33 MB

01. Mat HS-9P7UPWFu8w8.mp4

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Part 08-Module 01-Lesson 04_Convolutional Neural Networks

01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt

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01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt

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07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt

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23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt

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04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt

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22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt

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07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt

3.61 KB

12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt

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04. MLPs For Image Classification-TIFStebu530.en.vtt

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23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt

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07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt

3.84 KB

23. Visualizing CNNs-mnqS_EhEZVg.en.vtt

3.87 KB

22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt

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04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt

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05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt

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22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt

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12. Stride and Padding-0r9o8hprDXQ.en.vtt

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12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt

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15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt

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02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt

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06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt

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18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt

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05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt

4.82 KB

03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt

4.91 KB

05. Categorical Cross-Entropy-3sDYifgjFck.pt-BR.vtt

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02. Applications of CNNs-HrYNL_1SV2Y.en.vtt

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25. Transfer Learning-LHG5FltaR6I.zh-CN.vtt

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18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt

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15. Pooling Layers-OkkIZNs7Cyc.en.vtt

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06. Model Validation in Keras-002jNXSM6CU.en.vtt

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03. How Computers Interpret Images-V4f6p6uRhu8.en.vtt

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02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt

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26. Transfer Learning in Keras-HsIAznMM1LA.zh-CN.vtt

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index.html

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15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt

5.81 KB

03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt

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25. Transfer Learning-LHG5FltaR6I.en.vtt

6.00 KB

06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt

6.07 KB

10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt

6.10 KB

26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt

6.11 KB

18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt

6.12 KB

25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt

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26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt

6.77 KB

20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt

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10. Convolutional Layers-h5R_JvdUrUI.en.vtt

7.22 KB

10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt

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12. Stride and Padding.html

7.59 KB

09. Local Connectivity.html

7.59 KB

09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt

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10. Convolutional Layers (Part 1).html

7.62 KB

01. Introducing Alexis.html

7.79 KB

15. Pooling Layers.html

7.85 KB

07. When do MLPs (not) work well .html

7.93 KB

20. Image Augmentation in Keras-odStujZq3GY.en.vtt

8.22 KB

04. MLPs for Image Classification.html

8.29 KB

06. Model Validation in Keras.html

8.29 KB

19. Mini Project CNNs in Keras.html

8.39 KB

11. Convolutional Layers (Part 2).html

8.39 KB

21. Mini Project Image Augmentation in Keras.html

8.48 KB

20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt

8.49 KB

11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt

8.71 KB

22. Groundbreaking CNN Architectures.html

8.83 KB

09. Local Connectivity-z9wiDg0w-Dc.en.vtt

8.95 KB

03. How Computers Interpret Images.html

9.02 KB

26. Transfer Learning in Keras.html

9.07 KB

05. Categorical Cross-Entropy.html

9.11 KB

09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt

9.29 KB

18. CNNs in Keras Practical Example.html

9.37 KB

20. Image Augmentation in Keras.html

9.42 KB

17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt

9.72 KB

23. Visualizing CNNs (Part 1).html

9.81 KB

11. Convolutional Layers-RnM1D-XI--8.en.vtt

9.99 KB

17. CNNs for Image Classification.html

10.61 KB

16. Max Pooling Layers in Keras.html

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08. Mini Project Training an MLP on MNIST.html

10.88 KB

11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt

11.00 KB

17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt

11.37 KB

13. Convolutional Layers in Keras.html

11.95 KB

17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt

12.21 KB

02. Applications of CNNs.html

12.91 KB

24. Visualizing CNNs (Part 2).html

14.40 KB

14. Quiz Dimensionality.html

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25. Transfer Learning.html

18.80 KB

01. Introducing Alexis-38ExGpdyvJI.mp4

2.05 MB

04. MLPs For Image Classification-TIFStebu530.mp4

4.40 MB

06. Model Validation in Keras-002jNXSM6CU.mp4

5.20 MB

05. Categorical Cross-Entropy-3sDYifgjFck.mp4

5.42 MB

07. When do MLPs (not) work well-deMeuLdZN3Q.mp4

5.54 MB

15. Pooling Layers-OkkIZNs7Cyc.mp4

5.82 MB

03. How Computers Interpret Images-V4f6p6uRhu8.mp4

6.18 MB

12. Stride and Padding-0r9o8hprDXQ.mp4

7.98 MB

10. Convolutional Layers-h5R_JvdUrUI.mp4

8.04 MB

22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4

8.09 MB

18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4

8.71 MB

23. Visualizing CNNs-mnqS_EhEZVg.mp4

9.20 MB

20. Image Augmentation in Keras-odStujZq3GY.mp4

10.26 MB

26. Transfer Learning in Keras-HsIAznMM1LA.mp4

12.92 MB

09. Local Connectivity-z9wiDg0w-Dc.mp4

13.09 MB

25. Transfer Learning-LHG5FltaR6I.mp4

13.32 MB

02. Applications of CNNs-HrYNL_1SV2Y.mp4

17.70 MB

17. CNNs For Image Classification-l9vg_1YUlzg.mp4

18.16 MB

11. Convolutional Layers-RnM1D-XI--8.mp4

19.81 MB

Part 02-Module 04-Lesson 02_Intro to TensorFlow

13. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt

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13. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt

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18. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt

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17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt

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13. 13 L One Hot Encoding-phYsxqlilUk.en.vtt

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17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt

0.69 KB

18. Numerical Stability-_SbGcOS-jcQ.en-US.vtt

0.75 KB

21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt

0.75 KB

21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt

0.76 KB

01. Intro to Vincent-0_M6a04ofz8.zh-CN.vtt

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17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt

0.77 KB

18. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt

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01. Intro to Vincent-0_M6a04ofz8.en.vtt

0.81 KB

01. Intro to Vincent-0_M6a04ofz8.pt-BR.vtt

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21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt

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08. Supervised Classification-XTGsutypAPE.zh-CN.vtt

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04. Let'S Get Started-ySIDqaXLhHw.en.vtt

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04. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt

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02. What Is Deep Learning-INt1nULYPak.pt-BR.vtt

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02. What Is Deep Learning-INt1nULYPak.zh-CN.vtt

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02. What Is Deep Learning-INt1nULYPak.en.vtt

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23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.zh-CN.vtt

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24. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt

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09. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt

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24. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt

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23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt

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23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt

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09. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt

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03. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt

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24. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt

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16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt

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16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt

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16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt

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19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt

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22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.zh-CN.vtt

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22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.pt-BR.vtt

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19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.en.vtt

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19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.pt-BR.vtt

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20. 21 L Measuring Performance-byP0DJImOSk.zh-CN.vtt

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20. 21 L Measuring Performance-byP0DJImOSk.en-US.vtt

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index.html

5.83 KB

20. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt

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01. Intro to Vincent .html

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04. Let's Get Started .html

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02. What is Deep Learning .html

7.69 KB

24. Parameter Hyperspace .html

7.71 KB

08. Supervised Classification.html

7.71 KB

20. Measuring Performance .html

7.72 KB

16. Minimizing Cross Entropy.html

7.74 KB

22. Stochastic Gradient Descent.html

7.75 KB

03. Solving Problems - Big and Small .html

7.76 KB

09. Training Your Logistic Classifier .html

7.77 KB

23. Momentum and Learning Rate Decay.html

7.78 KB

21. Optimizing a Logistic Classifier.html

7.79 KB

19. Normalized Inputs and Initial Weights .html

7.79 KB

17. Practical Aspects of Learning.html

7.85 KB

18. Quiz Numerical Stability.html

8.44 KB

13. One-Hot Encoding.html

8.75 KB

07. Transition to Classification.html

8.90 KB

media

nmn.png

9.87 KB

05. Installing TensorFlow.html

10.10 KB

06. Hello, Tensor World!.html

10.24 KB

12. Quiz TensorFlow Softmax.html

10.49 KB

14. Categorical Cross-Entropy.html

10.80 KB

15. Quiz TensorFlow Cross Entropy.html

10.86 KB

27. Lab TensorFlow Neural Network.html

12.40 KB

26. Epochs.html

14.71 KB

11. ReLU and Softmax Activation Functions.html

16.51 KB

10. Quiz TensorFlow Linear Function.html

23.16 KB

25. Quiz Mini-batch.html

27.14 KB

18. Numerical Stability-_SbGcOS-jcQ.mp4

631.89 KB

13. 13 L One Hot Encoding-phYsxqlilUk.mp4

732.40 KB

08. Supervised Classification-XTGsutypAPE.mp4

1.32 MB

04. Let'S Get Started-ySIDqaXLhHw.mp4

1.45 MB

24. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4

2.23 MB

23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4

2.30 MB

09. Training Your Logistic Classifier-WQsdr1EJgz8.mp4

2.72 MB

01. Intro to Vincent-0_M6a04ofz8.mp4

3.08 MB

16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4

3.09 MB

21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4

3.42 MB

17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4

3.63 MB

22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4

3.92 MB

19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4

4.10 MB

20. 21 L Measuring Performance-byP0DJImOSk.mp4

5.50 MB

02. What Is Deep Learning-INt1nULYPak.mp4

5.78 MB

03. Solving Problems - Big And Small-WHcRQMGSbqg.mp4

9.17 MB

Part 09-Module 01-Lesson 02_Long Short-Term Memory Networks (LSTM)

07. Remember Gate-0qlm86HaXuU.zh-CN.vtt

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07. Remember Gate-0qlm86HaXuU.pt-BR.vtt

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07. Remember Gate-0qlm86HaXuU.en.vtt

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06. Forget Gate-iWxpfxLUPSU.zh-CN.vtt

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06. Forget Gate-iWxpfxLUPSU.en.vtt

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06. Forget Gate-iWxpfxLUPSU.pt-BR.vtt

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04. LSTM Architecture-ycwthhdx8ws.zh-CN.vtt

1.34 KB

04. LSTM Architecture-ycwthhdx8ws.pt-BR.vtt

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04. LSTM Architecture-ycwthhdx8ws.en.vtt

1.49 KB

08. LSTM 7 Use Gate-5Ifolm1jTdY.zh-CN.vtt

1.50 KB

08. LSTM 7 Use Gate-5Ifolm1jTdY.pt-BR.vtt

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08. LSTM 7 Use Gate-5Ifolm1jTdY.en.vtt

1.75 KB

11. Other Architectures-MsxFDuYlTuQ.zh-CN.vtt

2.04 KB

09. Putting It All Together-IF8FlKW-Zo0.zh-CN.vtt

2.13 KB

11. Other Architectures-MsxFDuYlTuQ.en.vtt

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09. Putting It All Together-IF8FlKW-Zo0.pt-BR.vtt

2.36 KB

09. Putting It All Together-IF8FlKW-Zo0.en.vtt

2.42 KB

11. Other Architectures-MsxFDuYlTuQ.pt-BR.vtt

2.45 KB

05. Learn Gate-aVHVI7ovbHY.zh-CN.vtt

2.51 KB

05. Learn Gate-aVHVI7ovbHY.en.vtt

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05. Learn Gate-aVHVI7ovbHY.pt-BR.vtt

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02. RNN Vs LSTM-70MgF-IwAr8.zh-CN.vtt

4.22 KB

02. RNN Vs LSTM-70MgF-IwAr8.pt-BR.vtt

4.24 KB

index.html

4.26 KB

03. LSTM Basics-gjb68a4XsqE.zh-CN.vtt

4.59 KB

02. RNN Vs LSTM-70MgF-IwAr8.en.vtt

4.71 KB

03. LSTM Basics-gjb68a4XsqE.pt-BR.vtt

5.06 KB

03. LSTM Basics-gjb68a4XsqE.en.vtt

5.21 KB

12. Outro LSTM.html

5.64 KB

02. RNN vs LSTM.html

5.66 KB

03. Basics of LSTM.html

5.67 KB

04. Architecture of LSTM.html

5.71 KB

09. Putting it All Together.html

5.75 KB

07. The Remember Gate.html

6.08 KB

11. Other architectures.html

6.12 KB

06. The Forget Gate.html

6.18 KB

05. The Learn Gate.html

6.26 KB

08. The Use Gate.html

6.30 KB

01. Intro to LSTM.html

6.44 KB

10. Quiz.html

7.32 KB

img

screen-shot-2017-11-16-at-4.27.58-pm.png

27.77 KB

screen-shot-2017-11-16-at-4.26.22-pm.png

41.24 KB

screen-shot-2017-11-16-at-4.31.41-pm.png

44.91 KB

screen-shot-2017-11-16-at-5.54.40-pm.png

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meme.png

209.05 KB

07. Remember Gate-0qlm86HaXuU.mp4

676.91 KB

06. Forget Gate-iWxpfxLUPSU.mp4

1.04 MB

04. LSTM Architecture-ycwthhdx8ws.mp4

1.07 MB

08. LSTM 7 Use Gate-5Ifolm1jTdY.mp4

1.50 MB

09. Putting It All Together-IF8FlKW-Zo0.mp4

1.58 MB

11. Other Architectures-MsxFDuYlTuQ.mp4

1.71 MB

05. Learn Gate-aVHVI7ovbHY.mp4

2.22 MB

02. RNN Vs LSTM-70MgF-IwAr8.mp4

3.58 MB

03. LSTM Basics-gjb68a4XsqE.mp4

4.03 MB

Part 03-Module 04-Lesson 04_Generate TV Scripts

01. Project-3-Intro-qNpv7IjQzo0.zh-CN.vtt

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01. Project-3-Intro-qNpv7IjQzo0.en.vtt

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01. Project-3-Intro-qNpv7IjQzo0.en-US.vtt

0.68 KB

01. Project-3-Intro-qNpv7IjQzo0.pt-BR.vtt

0.70 KB

index.html

3.76 KB

01. Introduction.html

5.13 KB

02. TV Script Workspace.html

5.44 KB

Project Description - Generate TV Scripts.html

6.53 KB

Project Rubric - Generate TV Scripts.html

12.85 KB

01. Project-3-Intro-qNpv7IjQzo0.mp4

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Part 07-Module 01-Lesson 06_TensorFlow

17. Conclusion-wOiUQDgGD9E.zh-CN.vtt

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17. Conclusion-wOiUQDgGD9E.en.vtt

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17. Conclusion-wOiUQDgGD9E.pt-BR.vtt

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img

linear-equation.gif

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mnist-012.png

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weights-0-1-2.png

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session.png

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relu-network.png

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softmax-input-output.png

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notmnist.png

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cross-entropy-diagram.png

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dropout-node.jpeg

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tensorflow.png

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meme.png

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multi-layer.png

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layers.png

286.10 KB

index.html

4.77 KB

17. Outro.html

6.31 KB

10. Lab NotMNIST in TensorFlow.html

6.80 KB

11. Two-layer Neural Network.html

8.12 KB

01. Intro.html

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09. Pre-Lab NotMNIST in TensorFlow.html

8.65 KB

02. Installing TensorFlow.html

8.81 KB

03. Hello, Tensor World!.html

8.95 KB

05. Quiz TensorFlow Softmax.html

9.20 KB

06. Quiz TensorFlow Cross Entropy.html

9.57 KB

12. Quiz TensorFlow ReLUs.html

9.81 KB

media

nmn.png

9.87 KB

15. Finetuning.html

10.49 KB

13. Deep Neural Network in TensorFlow.html

11.97 KB

08. Epochs.html

13.42 KB

16. Quiz TensorFlow Dropout.html

13.97 KB

14. Save and Restore TensorFlow Models.html

15.08 KB

04. Quiz TensorFlow Linear Function.html

21.87 KB

07. Quiz Mini-batch.html

25.85 KB

17. Conclusion-wOiUQDgGD9E.mp4

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Part 01-Module 02-Lesson 01_Regression

01. Welcome to Week One-10M2DnJuziE.zh-CN.vtt

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01. Welcome to Week One-10M2DnJuziE.pt-BR.vtt

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01. Welcome to Week One-10M2DnJuziE.en-US.vtt

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index.html

4.02 KB

01. Welcome to Week One.html

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03. Siraj's Intro to Deep Learning.html

5.69 KB

07. Siraj's Live Session.html

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02. Preparing for Siraj's video.html

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05. Linear Regression Warnings.html

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06. Multiple Linear Regression.html

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03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.pt.vtt

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04. Linear Regression.html

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03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.en.vtt

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img

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just-a-simple-lin-reg.png

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lin-reg-w-outliers.png

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lin-reg-no-outliers.png

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just-a-2d-reg.png

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01. Welcome to Week One-10M2DnJuziE.mp4

4.22 MB

03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.mp4

63.46 MB

Part 07-Module 01-Lesson 04_Sentiment Analysis

01. Introducing Andrew Trask-ltO71Bm8b3M.zh-CN.vtt

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01. Introducing Andrew Trask-ltO71Bm8b3M.en.vtt

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01. Introducing Andrew Trask-ltO71Bm8b3M.pt-BR.vtt

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23. Andrew Trask - Outro-nIF0GLOQglQ.pt-BR.vtt

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23. Andrew Trask - Outro-nIF0GLOQglQ.zh-CN.vtt

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23. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt

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11. Building a Neural Network-aM2k7RTjjJI.pt-BR.vtt

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11. Building a Neural Network-aM2k7RTjjJI.zh-CN.vtt

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11. Building a Neural Network-aM2k7RTjjJI.en.vtt

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02. Andrew Trask - Intro-da1I0mea1jQ.pt-BR.vtt

3.68 KB

10. Mini Project 2 Solution-45ihpPaeO8E.pt-BR.vtt

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08. Transforming Text into Numbers-7rHBU5cbePE.pt-BR.vtt

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08. Transforming Text into Numbers-7rHBU5cbePE.zh-CN.vtt

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10. Mini Project 2 Solution-45ihpPaeO8E.zh-CN.vtt

4.56 KB

02. Andrew Trask - Intro-da1I0mea1jQ.zh-CN.vtt

4.94 KB

08. Transforming Text into Numbers-7rHBU5cbePE.en.vtt

5.04 KB

10. Mini Project 2 Solution-45ihpPaeO8E.en.vtt

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index.html

5.26 KB

02. Andrew Trask - Intro-da1I0mea1jQ.en-US.vtt

5.42 KB

23. Conclusion.html

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05. Framing the Problem.html

7.00 KB

19. Further Noise Reduction.html

7.03 KB

01. Introducing Andrew Trask.html

7.04 KB

11. Building a Neural Network.html

7.04 KB

14. Understanding Neural Noise.html

7.05 KB

08. Transforming Text into Numbers.html

7.08 KB

16. Understanding Inefficiencies in our Network.html

7.17 KB

13. Mini Project 3 Solution.html

7.29 KB

10. Mini Project 2 Solution.html

7.29 KB

07. Mini Project 1 Solution.html

7.29 KB

18. Mini Project 5 Solution.html

7.29 KB

02. Meet Andrew.html

7.31 KB

04. The Notebooks.html

7.39 KB

22. Analysis What's Going on in the Weights.html

7.40 KB

21. Mini Project 6 Solution.html

7.41 KB

06. Mini Project 1.html

7.57 KB

07. Mini Project 1 Solution-l4r5l0HvHRI.pt-BR.vtt

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15. Mini Project 4.html

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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.pt-BR.vtt

7.84 KB

05. Framing the Problem-IsTOnkAKaJw.pt-BR.vtt

7.85 KB

18. Mini Project 5 Solution-Hv86B_jjWTI.zh-CN.vtt

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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.zh-CN.vtt

8.20 KB

18. Mini Project 5 Solution-Hv86B_jjWTI.pt-BR.vtt

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05. Framing the Problem-IsTOnkAKaJw.zh-CN.vtt

8.31 KB

17. Mini Project 5.html

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12. Mini Project 3.html

8.52 KB

20. Mini Project 6.html

8.59 KB

05. Framing the Problem-IsTOnkAKaJw.en.vtt

8.81 KB

07. Mini Project 1 Solution-l4r5l0HvHRI.en.vtt

8.83 KB

07. Mini Project 1 Solution-l4r5l0HvHRI.zh-CN.vtt

8.85 KB

03. Materials.html

9.20 KB

16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt

9.30 KB

09. Mini Project 2.html

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18. Mini Project 5 Solution-Hv86B_jjWTI.en.vtt

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19. Further Noise Reduction-Kl3hWxizKVg.zh-CN.vtt

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19. Further Noise Reduction-Kl3hWxizKVg.pt-BR.vtt

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19. Further Noise Reduction-Kl3hWxizKVg.en.vtt

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22. Analysis What's Going on in the Weights-UHsT35pbpcE.zh-CN.vtt

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21. Mini Project 6 Solution-ji0famK7gOQ.zh-CN.vtt

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21. Mini Project 6 Solution-ji0famK7gOQ.pt-BR.vtt

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22. Analysis What's Going on in the Weights-UHsT35pbpcE.pt-BR.vtt

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14. Understanding Neural Noise-ubqhh4Iv7O4.zh-CN.vtt

13.47 KB

22. Analysis What's Going on in the Weights-UHsT35pbpcE.en.vtt

13.69 KB

14. Understanding Neural Noise-ubqhh4Iv7O4.pt-BR.vtt

13.69 KB

21. Mini Project 6 Solution-ji0famK7gOQ.en.vtt

13.94 KB

14. Understanding Neural Noise-ubqhh4Iv7O4.en.vtt

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13. Mini Project 3 Solution-imnxzCev4SI.pt-BR.vtt

15.80 KB

13. Mini Project 3 Solution-imnxzCev4SI.zh-CN.vtt

15.84 KB

13. Mini Project 3 Solution-imnxzCev4SI.en.vtt

18.48 KB

img

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70.26 KB

01. Introducing Andrew Trask-ltO71Bm8b3M.mp4

4.08 MB

08. Transforming Text into Numbers-7rHBU5cbePE.mp4

6.64 MB

10. Mini Project 2 Solution-45ihpPaeO8E.mp4

9.22 MB

11. Building a Neural Network-aM2k7RTjjJI.mp4

10.17 MB

23. Andrew Trask - Outro-nIF0GLOQglQ.mp4

11.79 MB

05. Framing the Problem-IsTOnkAKaJw.mp4

17.65 MB

16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.mp4

20.67 MB

19. Further Noise Reduction-Kl3hWxizKVg.mp4

22.33 MB

02. Andrew Trask - Intro-da1I0mea1jQ.mp4

23.76 MB

07. Mini Project 1 Solution-l4r5l0HvHRI.mp4

24.79 MB

18. Mini Project 5 Solution-Hv86B_jjWTI.mp4

28.86 MB

22. Analysis What's Going on in the Weights-UHsT35pbpcE.mp4

33.67 MB

21. Mini Project 6 Solution-ji0famK7gOQ.mp4

39.15 MB

14. Understanding Neural Noise-ubqhh4Iv7O4.mp4

50.21 MB

13. Mini Project 3 Solution-imnxzCev4SI.mp4

54.58 MB

Part 11-Module 01-Lesson 02_The RL Framework The Problem

01. Introduction-X_9l_ZqXXBA.zh-CN.vtt

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01. Introduction-X_9l_ZqXXBA.en.vtt

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01. Introduction-X_9l_ZqXXBA.pt-BR.vtt

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17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt

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03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt

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07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt

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03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt

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17. MDPs, Part 3-UlXHFbla3QI.en.vtt

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06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt

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03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt

3.04 KB

07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt

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17. MDPs, Part 3-UlXHFbla3QI.pt-BR.vtt

3.38 KB

13. MDPs, Part 1-NBWbluSbxPg.zh-CN.vtt

3.38 KB

06. The Reward Hypothesis-uAqNwgZ49JE.en.vtt

3.45 KB

07. Goals and Rewards, Part 1-XPnj3Ya3EuM.pt-BR.vtt

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06. The Reward Hypothesis-uAqNwgZ49JE.pt-BR.vtt

3.68 KB

13. MDPs, Part 1-NBWbluSbxPg.en.vtt

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screen-shot-2017-09-21-at-3.08.03-pm.png

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pup.jpg

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screen-shot-2017-09-21-at-12.20.30-pm.png

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screen-shot-2017-09-21-at-12.20.50-pm.png

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go.jpg

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13. MDPs, Part 1-NBWbluSbxPg.pt-BR.vtt

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10. Cumulative Reward-ysriH65lV9o.zh-CN.vtt

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02. The Setting, Revisited-V6Q1uF8a6kA.zh-CN.vtt

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index.html

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08. Goals and Rewards, Part 2-pVIFc72VYH8.zh-CN.vtt

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10. Cumulative Reward-ysriH65lV9o.en.vtt

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02. The Setting, Revisited-V6Q1uF8a6kA.en.vtt

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14. MDPs, Part 2-CUTtQvxKkNw.zh-CN.vtt

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08. Goals and Rewards, Part 2-pVIFc72VYH8.en.vtt

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02. The Setting, Revisited-V6Q1uF8a6kA.pt-BR.vtt

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11. Discounted Return-opXGNPwwn7g.zh-CN.vtt

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10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt

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08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt

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14. MDPs, Part 2.html

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17. MDPs, Part 3.html

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10. Cumulative Reward.html

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06. The Reward Hypothesis.html

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02. The Setting, Revisited.html

6.58 KB

07. Goals and Rewards, Part 1.html

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03. Episodic vs. Continuing Tasks.html

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01. Introduction.html

6.77 KB

14. MDPs, Part 2-CUTtQvxKkNw.en.vtt

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11. Discounted Return-opXGNPwwn7g.en.vtt

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08. Goals and Rewards, Part 2.html

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14. MDPs, Part 2-CUTtQvxKkNw.pt-BR.vtt

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11. Discounted Return.html

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13. MDPs, Part 1.html

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11. Discounted Return-opXGNPwwn7g.pt-BR.vtt

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05. Quiz Episodic or Continuing.html

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15. Quiz One-Step Dynamics, Part 1.html

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18. Finite MDPs.html

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12. Quiz Pole-Balancing.html

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09. Quiz Goals and Rewards.html

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19. Summary.html

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04. Quiz Test Your Intuition.html

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16. Quiz One-Step Dynamics, Part 2.html

14.48 KB

01. Introduction-X_9l_ZqXXBA.mp4

2.90 MB

13. MDPs, Part 1-NBWbluSbxPg.mp4

3.86 MB

06. The Reward Hypothesis-uAqNwgZ49JE.mp4

4.38 MB

14. MDPs, Part 2-CUTtQvxKkNw.mp4

6.82 MB

07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4

6.84 MB

02. The Setting, Revisited-V6Q1uF8a6kA.mp4

7.36 MB

08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4

8.05 MB

10. Cumulative Reward-ysriH65lV9o.mp4

9.96 MB

03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4

10.07 MB

11. Discounted Return-opXGNPwwn7g.mp4

14.30 MB

17. MDPs, Part 3-UlXHFbla3QI.mp4

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Part 04-Module 02-Lesson 04_Generate Faces

01. Last Project - Congrats-UUqU8SYBZ9Q.zh-CN.vtt

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01. Last Project - Congrats-UUqU8SYBZ9Q.en.vtt

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01. Last Project - Congrats-UUqU8SYBZ9Q.pt-BR.vtt

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02. P5 Intro-jvJtHYBX7sM.en.vtt

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02. P5 Intro-jvJtHYBX7sM.zh-CN.vtt

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02. P5 Intro-jvJtHYBX7sM.pt-BR.vtt

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index.html

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02. Project Introduction.html

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01. One Project Away!.html

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03. Face Generation Workspace.html

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Project Description - Generate Faces.html

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Project Rubric - Generate Faces.html

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02. P5 Intro-jvJtHYBX7sM.mp4

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01. Last Project - Congrats-UUqU8SYBZ9Q.mp4

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Part 11-Module 01-Lesson 10_Policy-Based Methods

01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt

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06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt

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01. M2L3 01 V1-YOSREyp04HA.en.vtt

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06. M2L3 06 V1-RMjdQkl6CqE.en.vtt

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08. M2L3 08 V1-og3W6CXn1F0.zh-CN.vtt

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08. M2L3 08 V1-og3W6CXn1F0.en.vtt

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index.html

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04. M2L3 04 V1-QicxmyE5vTo.zh-CN.vtt

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03. M2L3 03 V2-TePX-0Bs23E.zh-CN.vtt

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08. Recap.html

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05. Policy Gradients.html

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01. Policy-Based Methods.html

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04. Stochastic Policy Search.html

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02. Why Policy-Based Methods.html

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06. Monte Carlo Policy Gradients.html

5.31 KB

07. Constrained Policy Gradients.html

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03. Policy Function Approximation.html

5.31 KB

04. M2L3 04 V1-QicxmyE5vTo.en.vtt

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03. M2L3 03 V2-TePX-0Bs23E.en.vtt

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05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt

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05. M2L3 05 V1-eZxxNNIZuwA.en.vtt

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07. M2L3 07 V2-ZBLLGIN1EfU.zh-CN.vtt

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02. M2L3 02 V2-ToS8vXGdODE.zh-CN.vtt

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02. M2L3 02 V2-ToS8vXGdODE.en.vtt

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07. M2L3 07 V2-ZBLLGIN1EfU.en.vtt

9.11 KB

06. M2L3 06 V1-RMjdQkl6CqE.mp4

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01. M2L3 01 V1-YOSREyp04HA.mp4

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08. M2L3 08 V1-og3W6CXn1F0.mp4

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05. M2L3 05 V1-eZxxNNIZuwA.mp4

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03. M2L3 03 V2-TePX-0Bs23E.mp4

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04. M2L3 04 V1-QicxmyE5vTo.mp4

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02. M2L3 02 V2-ToS8vXGdODE.mp4

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07. M2L3 07 V2-ZBLLGIN1EfU.mp4

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Part 11-Module 01-Lesson 05_Monte Carlo Methods

01. Introduction-W2EP3riQSus.zh-CN.vtt

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01. Introduction-W2EP3riQSus.en.vtt

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01. Introduction-W2EP3riQSus.pt-BR.vtt

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12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt

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09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt

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12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt

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09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt

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12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt

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09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt

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18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt

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18. MC Control Constant-alpha-QFV1nI9Zpoo.en.vtt

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18. MC Control Constant-alpha-QFV1nI9Zpoo.pt-BR.vtt

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10. MC Control Incremental Mean-E2RITH-2NUE.zh-CN.vtt

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06. MC Prediction Action Values-08tLtbh0xLs.zh-CN.vtt

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index.html

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10. MC Control Incremental Mean-E2RITH-2NUE.en.vtt

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13. MC Control Policy Improvement-2RKH-BInX7s.zh-CN.vtt

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10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt

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06. MC Prediction Action Values-08tLtbh0xLs.en.vtt

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13. MC Control Policy Improvement-2RKH-BInX7s.en.vtt

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06. MC Prediction Action Values-08tLtbh0xLs.pt-BR.vtt

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03. MC Prediction State Values.html

7.07 KB

10. MC Control Incremental Mean.html

7.07 KB

06. MC Prediction Action Values.html

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18. MC Control Constant-alpha, Part 1.html

7.08 KB

09. Generalized Policy Iteration.html

7.08 KB

12. MC Control Policy Evaluation.html

7.08 KB

13. MC Control Policy Improvement.html

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13. MC Control Policy Improvement-2RKH-BInX7s.pt-BR.vtt

7.10 KB

03. MC Prediction State Values-0q2wSWyuBj8.zh-CN.vtt

7.20 KB

01. Introduction.html

7.22 KB

17. Mini Project MC (Part 3).html

7.43 KB

21. Mini Project MC (Part 4).html

7.43 KB

08. Mini Project MC (Part 2).html

7.43 KB

05. Mini Project MC (Parts 0 and 1).html

7.44 KB

16. Implementation.html

7.81 KB

20. Implementation.html

7.89 KB

03. MC Prediction State Values-0q2wSWyuBj8.en.vtt

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07. Implementation.html

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03. MC Prediction State Values-0q2wSWyuBj8.pt-BR.vtt

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02. OpenAI Gym BlackjackEnv.html

9.33 KB

04. Implementation.html

9.45 KB

11. Quiz Incremental Mean.html

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14. Quiz Epsilon-Greedy Policies.html

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22. Summary.html

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19. MC Control Constant-alpha, Part 2.html

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15. Exploration vs. Exploitation.html

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img

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exploration-vs.-exploitation.png

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mc-control-glie.png

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01. Introduction-W2EP3riQSus.mp4

4.93 MB

09. Generalized Policy Iteration-XRmz4nolEsw.mp4

6.92 MB

12. MC Control Policy Evaluation-3_opwMzpEEI.mp4

9.10 MB

18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4

12.46 MB

10. MC Control Incremental Mean-E2RITH-2NUE.mp4

20.07 MB

13. MC Control Policy Improvement-2RKH-BInX7s.mp4

22.00 MB

06. MC Prediction Action Values-08tLtbh0xLs.mp4

22.01 MB

03. MC Prediction State Values-0q2wSWyuBj8.mp4

33.39 MB

Part 11-Module 01-Lesson 04_Dynamic Programming

01. Introduction-ek2PD9RDrWw.zh-CN.vtt

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01. Introduction-ek2PD9RDrWw.en.vtt

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01. Introduction-ek2PD9RDrWw.pt-BR.vtt

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04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt

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17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt

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04. Another Gridworld Example-n9SbomnLb-U.en.vtt

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17. Policy Iteration-gqv7o1kBDc0.en.vtt

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04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt

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17. Policy Iteration-gqv7o1kBDc0.pt-BR.vtt

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20. Truncated Policy Iteration-a-RvCxlPMho.zh-CN.vtt

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20. Truncated Policy Iteration-a-RvCxlPMho.en.vtt

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23. Value Iteration-XNeQn8N36y8.zh-CN.vtt

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20. Truncated Policy Iteration-a-RvCxlPMho.pt-BR.vtt

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23. Value Iteration-XNeQn8N36y8.en.vtt

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23. Value Iteration-XNeQn8N36y8.pt-BR.vtt

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index.html

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08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt

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05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt

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14. Policy Improvement-4_adUEK0IHg.zh-CN.vtt

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08. Iterative Policy Evaluation-eDXIL_oOJHI.en.vtt

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23. Value Iteration.html

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17. Policy Iteration.html

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14. Policy Improvement.html

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05. An Iterative Method, Part 1.html

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08. Iterative Policy Evaluation-eDXIL_oOJHI.pt-BR.vtt

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20. Truncated Policy Iteration.html

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08. Iterative Policy Evaluation.html

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05. An Iterative Method-AX-hG3KvwzY.en.vtt

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22. Mini Project DP (Part 5).html

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16. Mini Project DP (Part 3).html

7.79 KB

25. Mini Project DP (Part 6).html

7.79 KB

13. Mini Project DP (Part 2).html

7.79 KB

19. Mini Project DP (Part 4).html

7.79 KB

10. Mini Project DP (Parts 0 and 1).html

7.81 KB

04. Another Gridworld Example.html

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01. Introduction.html

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14. Policy Improvement-4_adUEK0IHg.en.vtt

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05. An Iterative Method-AX-hG3KvwzY.pt-BR.vtt

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18. Implementation.html

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14. Policy Improvement-4_adUEK0IHg.pt-BR.vtt

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12. Implementation.html

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26. Check Your Understanding.html

9.22 KB

15. Implementation.html

9.78 KB

21. Implementation.html

9.86 KB

03. Your Workspace.html

9.92 KB

24. Implementation.html

10.11 KB

02. OpenAI Gym FrozenLakeEnv.html

10.42 KB

07. Quiz An Iterative Method.html

11.47 KB

11. Action Values.html

12.16 KB

09. Implementation.html

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img

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est-action.png

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actionvalue.png

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statevalue.png

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06. An Iterative Method, Part 2.html

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27. Summary.html

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04. Another Gridworld Example-n9SbomnLb-U.mp4

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01. Introduction-ek2PD9RDrWw.mp4

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17. Policy Iteration-gqv7o1kBDc0.mp4

8.14 MB

20. Truncated Policy Iteration-a-RvCxlPMho.mp4

14.13 MB

23. Value Iteration-XNeQn8N36y8.mp4

15.65 MB

08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4

26.59 MB

05. An Iterative Method-AX-hG3KvwzY.mp4

27.57 MB

14. Policy Improvement-4_adUEK0IHg.mp4

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Part 11-Module 01-Lesson 11_Actor-Critic Methods

06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.zh-CN.vtt

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06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt

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01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt

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01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt

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01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt

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07. Summary-hvYQ_3LgCYs.zh-CN.vtt

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07. Summary-hvYQ_3LgCYs.en.vtt

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt

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07. Summary-hvYQ_3LgCYs.pt-BR.vtt

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt

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05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.zh-CN.vtt

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05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.en.vtt

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05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.pt-BR.vtt

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07. Summary.html

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02. A Better Score Function.html

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04. The Actor and The Critic.html

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05. Advantage Function.html

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03. Two Function Approximators.html

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01. Actor-Critic Methods.html

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06. Actor-Critic with Advantage.html

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06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4

10.01 MB

01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4

10.38 MB

05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4

10.72 MB

07. Summary-hvYQ_3LgCYs.mp4

16.90 MB

Part 01-Module 01-Lesson 01_Welcome

08. We Value Your Feedback-Dl23R0YCQ0U.zh-CN.vtt

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08. We Value Your Feedback-Dl23R0YCQ0U.pt-BR.vtt

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08. We Value Your Feedback-Dl23R0YCQ0U.en-US.vtt

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04. The first week-krK-TcGoYUI.zh-CN.vtt

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04. The first week-krK-TcGoYUI.pt-BR.vtt

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04. The first week-krK-TcGoYUI.en-US.vtt

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09. Getting-Setup-1SuxTnuQkeE.zh-CN.vtt

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09. Getting-Setup-1SuxTnuQkeE.pt-BR.vtt

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09. Getting-Setup-1SuxTnuQkeE.en.vtt

1.34 KB

01. Welcome-PdPdogFHnvE.zh-CN.vtt

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03. Meet Your Instructors -EcP0U4720sA.pt-BR.vtt

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03. Meet Your Instructors -EcP0U4720sA.zh-CN.vtt

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03. Meet Your Instructors -EcP0U4720sA.en.vtt

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01. Welcome-PdPdogFHnvE.pt-BR.vtt

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03. Meet Your Instructors -EcP0U4720sA.en-US.vtt

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01. Welcome-PdPdogFHnvE.en.vtt

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02. Projects You Will Build-yDPuDuCMST8.en.vtt

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index.html

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01. Welcome to the Deep Learning Nanodegree Foundations.html

5.54 KB

08. We Value Your Feedback.html

5.88 KB

09. Getting Set Up.html

5.89 KB

05. Prerequisites.html

6.01 KB

03. Meet Your Instructors.html

6.01 KB

04. The First Week.html

6.95 KB

02. Projects You Will Build .html

7.29 KB

06. Community Support.html

12.22 KB

07. Deadline Policy.html

16.21 KB

img

view.png

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09. Getting-Setup-1SuxTnuQkeE.mp4

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08. We Value Your Feedback-Dl23R0YCQ0U.mp4

6.85 MB

04. The first week-krK-TcGoYUI.mp4

7.07 MB

02. Projects You Will Build-yDPuDuCMST8.mp4

8.90 MB

03. Meet Your Instructors -EcP0U4720sA.mp4

13.34 MB

01. Welcome-PdPdogFHnvE.mp4

17.58 MB

Part 04-Module 01-Lesson 01_Generative Adversarial Networks

09. Discriminator Network-nWXxT8OqCfs.pt-BR.vtt

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09. Discriminator Network-nWXxT8OqCfs.zh-CN.vtt

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09. Discriminator Network-nWXxT8OqCfs.en.vtt

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01. GANs Intro-F7XgI6TmaGI.pt-BR.vtt

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01. GANs Intro-F7XgI6TmaGI.zh-CN.vtt

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01. GANs Intro-F7XgI6TmaGI.en-US.vtt

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14. Training Optimizers-AU5gH7LS57E.zh-CN.vtt

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14. Training Optimizers-AU5gH7LS57E.pt-BR.vtt

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12. Building the Network Solution-Ikp3rVzG970.pt-BR.vtt

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14. Training Optimizers-AU5gH7LS57E.en.vtt

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12. Building the Network Solution-Ikp3rVzG970.zh-CN.vtt

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13. Training Losses-IaAeDrXMEcU.zh-CN.vtt

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11. Building the Network-5sZkRSHfiAE.pt-BR.vtt

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12. Building the Network Solution-Ikp3rVzG970.en.vtt

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11. Building the Network-5sZkRSHfiAE.zh-CN.vtt

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13. Training Losses-IaAeDrXMEcU.pt-BR.vtt

3.60 KB

07. Getting Started with GANs-QA2ntKUha4g.zh-CN.vtt

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13. Training Losses-IaAeDrXMEcU.en.vtt

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07. Getting Started with GANs-QA2ntKUha4g.pt-BR.vtt

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11. Building the Network-5sZkRSHfiAE.en.vtt

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07. Getting Started with GANs-QA2ntKUha4g.en.vtt

4.00 KB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.zh-CN.vtt

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index.html

4.88 KB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.pt-BR.vtt

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15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.en.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.pt-BR.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.zh-CN.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.en.vtt

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08. Generator Network-btHVXnICmzQ.pt-BR.vtt

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08. Generator Network-btHVXnICmzQ.zh-CN.vtt

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03. Other Generative Models, How GANs Work-MF0QCP1OC9I.zh-CN.vtt

6.41 KB

16. A Trained GAN.html

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13. Training Losses.html

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08. Generator Network.html

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14. Training Optimizers.html

6.47 KB

11. Building the Network.html

6.47 KB

09. Discriminator Network.html

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07. Get started with a GAN.html

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05. Practical tips and tricks for training GANs.html

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02. What can you do with GANs.html

6.52 KB

12. Building the Network Solution.html

6.54 KB

01. Introducing Ian Goodfellow.html

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04. Games and Equilibria.html

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10. Generator and Discriminator Solutions.html

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15. Training Losses and Optimizers Solution.html

6.61 KB

08. Generator Network-btHVXnICmzQ.en.vtt

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03. How GANs work.html

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02. Cool Things To Do With GANs-bo-ToTdhgew.pt-BR.vtt

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02. Cool Things To Do With GANs-bo-ToTdhgew.zh-CN.vtt

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06. Build a GAN.html

7.21 KB

02. Cool Things To Do With GANs-bo-ToTdhgew.en.vtt

7.34 KB

03. Other Generative Models, How GANs Work-MF0QCP1OC9I.en.vtt

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03. Other Generative Models, How GANs Work-MF0QCP1OC9I.pt-BR.vtt

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17. Doing More With Your GAN.html

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16. A Trained GAN-TR-uEJcjig4.zh-CN.vtt

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16. A Trained GAN-TR-uEJcjig4.pt-BR.vtt

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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.zh-CN.vtt

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16. A Trained GAN-TR-uEJcjig4.en.vtt

9.95 KB

05. GANs Architecture -gaEs7ccZv_Q.zh-CN.vtt

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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.en.vtt

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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.pt-BR.vtt

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05. GANs Architecture -gaEs7ccZv_Q.en.vtt

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05. GANs Architecture -gaEs7ccZv_Q.pt-BR.vtt

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09. Discriminator Network-nWXxT8OqCfs.mp4

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01. GANs Intro-F7XgI6TmaGI.mp4

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14. Training Optimizers-AU5gH7LS57E.mp4

3.45 MB

11. Building the Network-5sZkRSHfiAE.mp4

4.22 MB

12. Building the Network Solution-Ikp3rVzG970.mp4

4.33 MB

07. Getting Started with GANs-QA2ntKUha4g.mp4

5.04 MB

13. Training Losses-IaAeDrXMEcU.mp4

5.83 MB

10. Generator and Discriminator Solutions-9By2pAck044.mp4

6.29 MB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.mp4

6.82 MB

08. Generator Network-btHVXnICmzQ.mp4

7.08 MB

03. Other Generative Models, How GANs Work-MF0QCP1OC9I.mp4

10.28 MB

16. A Trained GAN-TR-uEJcjig4.mp4

11.67 MB

02. Cool Things To Do With GANs-bo-ToTdhgew.mp4

11.98 MB

04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.mp4

13.40 MB

05. GANs Architecture -gaEs7ccZv_Q.mp4

15.96 MB

Part 10-Module 01-Lesson 01_Generative Adversarial Networks

09. Discriminator Network-nWXxT8OqCfs.pt-BR.vtt

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09. Discriminator Network-nWXxT8OqCfs.zh-CN.vtt

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09. Discriminator Network-nWXxT8OqCfs.en.vtt

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01. GANs Intro-F7XgI6TmaGI.pt-BR.vtt

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01. GANs Intro-F7XgI6TmaGI.en.vtt

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01. GANs Intro-F7XgI6TmaGI.zh-CN.vtt

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01. GANs Intro-F7XgI6TmaGI.en-US.vtt

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14. Training Optimizers-AU5gH7LS57E.zh-CN.vtt

2.67 KB

14. Training Optimizers-AU5gH7LS57E.pt-BR.vtt

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12. Building the Network Solution-Ikp3rVzG970.pt-BR.vtt

2.89 KB

14. Training Optimizers-AU5gH7LS57E.en.vtt

3.04 KB

12. Building the Network Solution-Ikp3rVzG970.zh-CN.vtt

3.08 KB

13. Training Losses-IaAeDrXMEcU.zh-CN.vtt

3.33 KB

11. Building the Network-5sZkRSHfiAE.pt-BR.vtt

3.37 KB

12. Building the Network Solution-Ikp3rVzG970.en.vtt

3.43 KB

11. Building the Network-5sZkRSHfiAE.zh-CN.vtt

3.45 KB

13. Training Losses-IaAeDrXMEcU.pt-BR.vtt

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07. Getting Started with GANs-QA2ntKUha4g.zh-CN.vtt

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13. Training Losses-IaAeDrXMEcU.en.vtt

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07. Getting Started with GANs-QA2ntKUha4g.pt-BR.vtt

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11. Building the Network-5sZkRSHfiAE.en.vtt

3.83 KB

07. Getting Started with GANs-QA2ntKUha4g.en.vtt

4.00 KB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.zh-CN.vtt

4.59 KB

index.html

4.88 KB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.pt-BR.vtt

4.99 KB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.en.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.pt-BR.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.zh-CN.vtt

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10. Generator and Discriminator Solutions-9By2pAck044.en.vtt

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08. Generator Network-btHVXnICmzQ.pt-BR.vtt

6.01 KB

08. Generator Network-btHVXnICmzQ.zh-CN.vtt

6.08 KB

03. Other Generative Models, How GANs Work-MF0QCP1OC9I.zh-CN.vtt

6.41 KB

16. A Trained GAN.html

6.43 KB

13. Training Losses.html

6.44 KB

08. Generator Network.html

6.45 KB

14. Training Optimizers.html

6.47 KB

11. Building the Network.html

6.47 KB

09. Discriminator Network.html

6.48 KB

07. Get started with a GAN.html

6.50 KB

05. Practical tips and tricks for training GANs.html

6.51 KB

02. What can you do with GANs.html

6.52 KB

12. Building the Network Solution.html

6.54 KB

01. Introducing Ian Goodfellow.html

6.55 KB

04. Games and Equilibria.html

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10. Generator and Discriminator Solutions.html

6.59 KB

15. Training Losses and Optimizers Solution.html

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08. Generator Network-btHVXnICmzQ.en.vtt

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03. How GANs work.html

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02. Cool Things To Do With GANs-bo-ToTdhgew.pt-BR.vtt

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02. Cool Things To Do With GANs-bo-ToTdhgew.zh-CN.vtt

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06. Build a GAN.html

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02. Cool Things To Do With GANs-bo-ToTdhgew.en.vtt

7.34 KB

03. Other Generative Models, How GANs Work-MF0QCP1OC9I.en.vtt

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03. Other Generative Models, How GANs Work-MF0QCP1OC9I.pt-BR.vtt

7.48 KB

17. Doing More With Your GAN.html

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16. A Trained GAN-TR-uEJcjig4.zh-CN.vtt

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16. A Trained GAN-TR-uEJcjig4.pt-BR.vtt

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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.zh-CN.vtt

9.47 KB

16. A Trained GAN-TR-uEJcjig4.en.vtt

9.95 KB

05. GANs Architecture -gaEs7ccZv_Q.zh-CN.vtt

10.22 KB

04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.en.vtt

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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.pt-BR.vtt

11.23 KB

05. GANs Architecture -gaEs7ccZv_Q.en.vtt

11.75 KB

05. GANs Architecture -gaEs7ccZv_Q.pt-BR.vtt

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img

mat-headshot.png

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generated-mnist.png

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09. Discriminator Network-nWXxT8OqCfs.mp4

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01. GANs Intro-F7XgI6TmaGI.mp4

3.24 MB

14. Training Optimizers-AU5gH7LS57E.mp4

3.45 MB

11. Building the Network-5sZkRSHfiAE.mp4

4.22 MB

12. Building the Network Solution-Ikp3rVzG970.mp4

4.33 MB

07. Getting Started with GANs-QA2ntKUha4g.mp4

5.04 MB

13. Training Losses-IaAeDrXMEcU.mp4

5.83 MB

10. Generator and Discriminator Solutions-9By2pAck044.mp4

6.29 MB

15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.mp4

6.82 MB

08. Generator Network-btHVXnICmzQ.mp4

7.08 MB

03. Other Generative Models, How GANs Work-MF0QCP1OC9I.mp4

10.28 MB

16. A Trained GAN-TR-uEJcjig4.mp4

11.67 MB

02. Cool Things To Do With GANs-bo-ToTdhgew.mp4

11.98 MB

04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.mp4

13.40 MB

05. GANs Architecture -gaEs7ccZv_Q.mp4

15.96 MB

Part 03-Module 07-Lesson 03_Translation Project

01. Machine Translation Intro-5thBwpcYoiI.pt-BR.vtt

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01. Machine Translation Intro-5thBwpcYoiI.zh-CN.vtt

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01. Machine Translation Intro-5thBwpcYoiI.en.vtt

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index.html

3.69 KB

01. Introduction.html

4.96 KB

Project Description - Translation Project.html

5.94 KB

Project Rubric - Translation Project.html

9.59 KB

01. Machine Translation Intro-5thBwpcYoiI.mp4

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Part 11-Module 01-Lesson 06_Temporal-Difference Methods

13. TD Control Expected Sarsa-kEKupCyU0P0.zh-CN.vtt

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13. TD Control Expected Sarsa-kEKupCyU0P0.en.vtt

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07. TD Control Sarsa(0)-LkFkjfsRpXc.zh-CN.vtt

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07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt

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06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt

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01. Introduction-yXErXQulI_o.zh-CN.vtt

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06. TD Prediction Action Values-1c029-7_9GA.en.vtt

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01. Introduction-yXErXQulI_o.en.vtt

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10. TD Control Sarsamax-4DxoYuR7aZ4.zh-CN.vtt

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10. TD Control Sarsamax-4DxoYuR7aZ4.en.vtt

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index.html

4.77 KB

03. TD Prediction TD(0).html

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07. TD Control Sarsa(0).html

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01. Introduction.html

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13. TD Control Expected Sarsa.html

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10. TD Control Sarsamax.html

6.55 KB

06. TD Prediction Action Values.html

6.60 KB

12. Mini Project TD (Part 3).html

6.77 KB

15. Mini Project TD (Part 4).html

6.77 KB

09. Mini Project TD (Part 2).html

6.77 KB

05. Mini Project TD (Parts 0 and 1).html

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11. Implementation.html

7.09 KB

03. TD Prediction TD(0)-CsD6b0csU7o.zh-CN.vtt

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14. Implementation.html

7.73 KB

04. Implementation.html

8.08 KB

02. OpenAI Gym CliffWalkingEnv.html

8.28 KB

16. Analyzing Performance.html

8.54 KB

08. Implementation.html

8.61 KB

03. TD Prediction TD(0)-CsD6b0csU7o.en.vtt

8.83 KB

17. Summary.html

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img

screen-shot-2017-10-17-at-11.02.44-am.png

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matengai-of-kuniga-coast-in-oki-island-shimane-pref600.jpg

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expected-sarsa.png

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sarsamax.png

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sarsa.png

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td-prediction.png

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13. TD Control Expected Sarsa-kEKupCyU0P0.mp4

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07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4

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06. TD Prediction Action Values-1c029-7_9GA.mp4

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10. TD Control Sarsamax-4DxoYuR7aZ4.mp4

16.53 MB

01. Introduction-yXErXQulI_o.mp4

20.67 MB

03. TD Prediction TD(0)-CsD6b0csU7o.mp4

30.11 MB

Part 11-Module 01-Lesson 09_Deep Q-Learning

13. Wrap Up-x6JggcDTcys.zh-CN.vtt

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13. Wrap Up-x6JggcDTcys.en.vtt

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt

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13. Wrap Up-x6JggcDTcys.pt-BR.vtt

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt

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03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt

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03. Monte Carlo Learning-qOviWYwcvsg.en.vtt

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03. Monte Carlo Learning-qOviWYwcvsg.pt-BR.vtt

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt

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04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt

3.73 KB

09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.zh-CN.vtt

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt

3.93 KB

04. Temporal Difference Learning-lpmDi0QeUm8.en.vtt

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index.html

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04. Temporal Difference Learning-lpmDi0QeUm8.pt-BR.vtt

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09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.en.vtt

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05. Q-Learning-AI5gLgYMSq8.zh-CN.vtt

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08. Fixed Q Targets-SWpyiEezfp4.zh-CN.vtt

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06. Deep Q Network-GgtR_d1OB-M.zh-CN.vtt

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05. Q-Learning-AI5gLgYMSq8.en.vtt

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09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.pt-BR.vtt

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08. Fixed Q Targets-SWpyiEezfp4.en.vtt

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05. Q-Learning-AI5gLgYMSq8.pt-BR.vtt

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06. Deep Q Network-GgtR_d1OB-M.en.vtt

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13. Wrap Up.html

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08. Fixed Q Targets-SWpyiEezfp4.pt-BR.vtt

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01. Intro to Deep Q-Learning.html

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04. Temporal Difference Learning.html

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02. Neural Nets as Value Functions.html

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08. Fixed Q Targets.html

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05. Q-Learning.html

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03. Monte Carlo Learning.html

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06. Deep Q Network.html

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07. Experience Replay.html

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06. Deep Q Network-GgtR_d1OB-M.pt-BR.vtt

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12. TensorFlow Implementation.html

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09. Deep Q-Learning Algorithm.html

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10. DQN Improvements.html

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11. Implementing Deep Q-Learning.html

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07. Experience Replay-wX_-SZG-YMQ.zh-CN.vtt

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07. Experience Replay-wX_-SZG-YMQ.en.vtt

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10. DQN Improvements-Zfdbp93A2GU.zh-CN.vtt

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07. Experience Replay-wX_-SZG-YMQ.pt-BR.vtt

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10. DQN Improvements-Zfdbp93A2GU.en.vtt

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10. DQN Improvements-Zfdbp93A2GU.pt-BR.vtt

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img

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atari-network.png

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13. Wrap Up-x6JggcDTcys.mp4

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4

9.08 MB

03. Monte Carlo Learning-qOviWYwcvsg.mp4

10.41 MB

02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4

12.65 MB

04. Temporal Difference Learning-lpmDi0QeUm8.mp4

16.98 MB

05. Q-Learning-AI5gLgYMSq8.mp4

17.31 MB

09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4

17.45 MB

08. Fixed Q Targets-SWpyiEezfp4.mp4

20.97 MB

06. Deep Q Network-GgtR_d1OB-M.mp4

25.67 MB

10. DQN Improvements-Zfdbp93A2GU.mp4

39.40 MB

07. Experience Replay-wX_-SZG-YMQ.mp4

48.38 MB

Part 11-Module 01-Lesson 03_The RL Framework The Solution

01. Introduction-9Wyf5Zsska8.zh-CN.vtt

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01. Introduction-9Wyf5Zsska8.en.vtt

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01. Introduction-9Wyf5Zsska8.pt-BR.vtt

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04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt

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04. Gridworld Example-XeHBmPFqTsE.en.vtt

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04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt

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06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt

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11. Optimal Policies-2rguYpVyCto.zh-CN.vtt

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06. Bellman Equations-UgIaDMvSdUo.en.vtt

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08. Optimality-j231aRV74QM.zh-CN.vtt

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06. Bellman Equations-UgIaDMvSdUo.pt-BR.vtt

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05. State-Value Functions-llakAjwox_8.zh-CN.vtt

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11. Optimal Policies-2rguYpVyCto.en.vtt

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09. Action-Value Functions-KJLaRfOOPGA.zh-CN.vtt

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02. Policies-hc3LrvaC13U.zh-CN.vtt

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11. Optimal Policies-2rguYpVyCto.pt-BR.vtt

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index.html

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08. Optimality-j231aRV74QM.en.vtt

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09. Action-Value Functions-KJLaRfOOPGA.en.vtt

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05. State-Value Functions-llakAjwox_8.en.vtt

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02. Policies-hc3LrvaC13U.en.vtt

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08. Optimality-j231aRV74QM.pt-BR.vtt

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05. State-Value Functions-llakAjwox_8.pt-BR.vtt

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09. Action-Value Functions-KJLaRfOOPGA.pt-BR.vtt

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02. Policies-hc3LrvaC13U.pt-BR.vtt

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02. Policies.html

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08. Optimality.html

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11. Optimal Policies.html

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04. Gridworld Example.html

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01. Introduction.html

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09. Action-Value Functions.html

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10. Quiz Action-Value Functions.html

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05. State-Value Functions.html

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06. Bellman Equations.html

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13. Summary.html

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03. Quiz Interpret the Policy.html

11.67 KB

07. Quiz State-Value Functions.html

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12. Quiz Optimal Policies.html

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img

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screen-shot-2017-09-21-at-12.20.30-pm.png

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screen-shot-2017-09-24-at-4.28.04-pm.png

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04. Gridworld Example-XeHBmPFqTsE.mp4

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06. Bellman Equations-UgIaDMvSdUo.mp4

4.14 MB

05. State-Value Functions-llakAjwox_8.mp4

5.28 MB

01. Introduction-9Wyf5Zsska8.mp4

5.39 MB

08. Optimality-j231aRV74QM.mp4

5.99 MB

09. Action-Value Functions-KJLaRfOOPGA.mp4

6.60 MB

11. Optimal Policies-2rguYpVyCto.mp4

7.11 MB

02. Policies-hc3LrvaC13U.mp4

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Part 02-Module 02-Lesson 01_Sentiment Analysis with Andrew Trask

01. Introducing Andrew Trask-U3PqQF-8qyI.zh-CN.vtt

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01. Introducing Andrew Trask-U3PqQF-8qyI.en.vtt

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01. Introducing Andrew Trask-U3PqQF-8qyI.pt-BR.vtt

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22. Andrew Trask - Outro-nIF0GLOQglQ.pt-BR.vtt

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22. Andrew Trask - Outro-nIF0GLOQglQ.zh-CN.vtt

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22. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt

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10. Building a Neural Network-aM2k7RTjjJI.pt-BR.vtt

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10. Building a Neural Network-aM2k7RTjjJI.zh-CN.vtt

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10. Building a Neural Network-aM2k7RTjjJI.en.vtt

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02. Andrew Trask - Intro-da1I0mea1jQ.pt-BR.vtt

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09. Mini Project 2 Solution-45ihpPaeO8E.pt-BR.vtt

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07. Transforming Text into Numbers-7rHBU5cbePE.pt-BR.vtt

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07. Transforming Text into Numbers-7rHBU5cbePE.zh-CN.vtt

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09. Mini Project 2 Solution-45ihpPaeO8E.zh-CN.vtt

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02. Andrew Trask - Intro-da1I0mea1jQ.zh-CN.vtt

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07. Transforming Text into Numbers-7rHBU5cbePE.en.vtt

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09. Mini Project 2 Solution-45ihpPaeO8E.en.vtt

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index.html

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02. Andrew Trask - Intro-da1I0mea1jQ.en-US.vtt

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22. Conclusion.html

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04. Framing the Problem.html

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18. Further Noise Reduction.html

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10. Building a Neural Network.html

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13. Understanding Neural Noise.html

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07. Transforming Text into Numbers.html

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15. Understanding Inefficiencies in our Network.html

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06. Mini Project 1 Solution.html

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09. Mini Project 2 Solution.html

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12. Mini Project 3 Solution.html

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17. Mini Project 5 Solution.html

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02. Meet Andrew.html

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01. Introducing Andrew Trask.html

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21. Analysis What's Going on in the Weights.html

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20. Mini Project 6 Solution.html

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05. Mini Project 1.html

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14. Mini Project 4.html

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06. Mini Project 1 Solution-l4r5l0HvHRI.pt-BR.vtt

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15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.pt-BR.vtt

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04. Framing the Problem-IsTOnkAKaJw.pt-BR.vtt

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17. Mini Project 5 Solution-Hv86B_jjWTI.zh-CN.vtt

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15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.zh-CN.vtt

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17. Mini Project 5 Solution-Hv86B_jjWTI.pt-BR.vtt

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04. Framing the Problem-IsTOnkAKaJw.zh-CN.vtt

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16. Mini Project 5.html

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19. Mini Project 6.html

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04. Framing the Problem-IsTOnkAKaJw.en.vtt

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06. Mini Project 1 Solution-l4r5l0HvHRI.en.vtt

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06. Mini Project 1 Solution-l4r5l0HvHRI.zh-CN.vtt

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03. Materials.html

8.88 KB

15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt

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08. Mini Project 2.html

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17. Mini Project 5 Solution-Hv86B_jjWTI.en.vtt

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18. Further Noise Reduction-Kl3hWxizKVg.zh-CN.vtt

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18. Further Noise Reduction-Kl3hWxizKVg.pt-BR.vtt

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18. Further Noise Reduction-Kl3hWxizKVg.en.vtt

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21. Analysis What's Going on in the Weights-UHsT35pbpcE.zh-CN.vtt

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20. Mini Project 6 Solution-ji0famK7gOQ.zh-CN.vtt

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20. Mini Project 6 Solution-ji0famK7gOQ.pt-BR.vtt

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21. Analysis What's Going on in the Weights-UHsT35pbpcE.pt-BR.vtt

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13. Understanding Neural Noise-ubqhh4Iv7O4.zh-CN.vtt

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21. Analysis What's Going on in the Weights-UHsT35pbpcE.en.vtt

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13. Understanding Neural Noise-ubqhh4Iv7O4.pt-BR.vtt

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20. Mini Project 6 Solution-ji0famK7gOQ.en.vtt

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13. Understanding Neural Noise-ubqhh4Iv7O4.en.vtt

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12. Mini Project 3 Solution-imnxzCev4SI.pt-BR.vtt

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12. Mini Project 3 Solution-imnxzCev4SI.zh-CN.vtt

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12. Mini Project 3 Solution-imnxzCev4SI.en.vtt

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07. Transforming Text into Numbers-7rHBU5cbePE.mp4

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09. Mini Project 2 Solution-45ihpPaeO8E.mp4

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10. Building a Neural Network-aM2k7RTjjJI.mp4

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01. Introducing Andrew Trask-U3PqQF-8qyI.mp4

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22. Andrew Trask - Outro-nIF0GLOQglQ.mp4

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04. Framing the Problem-IsTOnkAKaJw.mp4

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15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.mp4

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18. Further Noise Reduction-Kl3hWxizKVg.mp4

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02. Andrew Trask - Intro-da1I0mea1jQ.mp4

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06. Mini Project 1 Solution-l4r5l0HvHRI.mp4

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17. Mini Project 5 Solution-Hv86B_jjWTI.mp4

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21. Analysis What's Going on in the Weights-UHsT35pbpcE.mp4

33.67 MB

20. Mini Project 6 Solution-ji0famK7gOQ.mp4

39.15 MB

13. Understanding Neural Noise-ubqhh4Iv7O4.mp4

50.21 MB

12. Mini Project 3 Solution-imnxzCev4SI.mp4

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Part 06-Module 01-Lesson 01_Welcome to Deep Learning

10. Getting-Setup-1SuxTnuQkeE.zh-CN.vtt

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02. Meet Your Instructors--UOFRxCu414.zh-CN.vtt

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02. Meet Your Instructors--UOFRxCu414.en.vtt

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02. Meet Your Instructors--UOFRxCu414.pt-BR.vtt

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05. Projects You will Build-PqpdX7YxTlU.zh-CN.vtt

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05. Projects You will Build-PqpdX7YxTlU.en.vtt

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index.html

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01. Welcome to the Deep Learning Nanodegree Program.html

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02. Meet Your Instructors.html

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10. Getting Set Up.html

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05. Projects You Will Build.html

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09. Prerequisites.html

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08. Community Guidelines.html

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03. Learning Plan.html

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07. Udacity Support.html

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04. Program Structure.html

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06. Deadline Policy.html

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img

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faces.png

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word-embeddings.jpg

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rnn.png

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examples.jpg

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02. Meet Your Instructors--UOFRxCu414.mp4

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05. Projects You will Build-PqpdX7YxTlU.mp4

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10. Getting-Setup-1SuxTnuQkeE.mp4

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01. 01 Welcome To The Deep Learning Program-3QPEmwq2NaE.mp4

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Part 11-Module 01-Lesson 08_RL in Continuous Spaces

13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt

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14. Summary-MTEBk43oByU.zh-CN.vtt

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14. Summary-MTEBk43oByU.en.vtt

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13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt

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14. Summary-MTEBk43oByU.pt-BR.vtt

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12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt

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09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt

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12. Kernel Functions-RdkPVYyVOvU.en.vtt

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09. Coarse Coding-Uu1J5KLAfTU.en.vtt

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07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt

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09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt

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10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt

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07. Tile Coding-BRs7AnTZ_8k.en.vtt

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05. Discretization-j2eZyUpy--E.zh-CN.vtt

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07. Tile Coding-BRs7AnTZ_8k.pt-BR.vtt

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index.html

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05. Discretization-j2eZyUpy--E.en.vtt

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10. Function Approximation-UTGWVY6jEdg.pt-BR.vtt

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05. Discretization-j2eZyUpy--E.pt-BR.vtt

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01. Deep Reinforcement Learning-GPjK124RU5g.zh-CN.vtt

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03. Discrete vs. Continuous Spaces-uHstLeRzaE8.zh-CN.vtt

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11. Linear Function Approximation-OJ5wrB7o-pI.zh-CN.vtt

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14. Summary.html

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07. Tile Coding.html

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09. Coarse Coding.html

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05. Discretization.html

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12. Kernel Functions.html

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11. Linear Function Approximation.html

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03. Discrete vs. Continuous Spaces.html

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13. Non-Linear Function Approximation.html

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08. Exercise Tile Coding.html

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06. Exercise Discretization.html

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01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt

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01. Deep Reinforcement Learning.html

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03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt

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11. Linear Function Approximation-OJ5wrB7o-pI.en.vtt

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04. Quiz Space Representations.html

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01. Deep Reinforcement Learning-GPjK124RU5g.pt-BR.vtt

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10. Function Approximation.html

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03. Discrete vs. Continuous Spaces-uHstLeRzaE8.pt-BR.vtt

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11. Linear Function Approximation-OJ5wrB7o-pI.pt-BR.vtt

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02. Resources.html

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img

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13. Non-Linear Function Approximation-rITnmpD2mN8.mp4

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12. Kernel Functions-RdkPVYyVOvU.mp4

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14. Summary-MTEBk43oByU.mp4

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09. Coarse Coding-Uu1J5KLAfTU.mp4

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07. Tile Coding-BRs7AnTZ_8k.mp4

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05. Discretization-j2eZyUpy--E.mp4

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10. Function Approximation-UTGWVY6jEdg.mp4

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03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4

21.37 MB

11. Linear Function Approximation-OJ5wrB7o-pI.mp4

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01. Deep Reinforcement Learning-GPjK124RU5g.mp4

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Part 09-Module 01-Lesson 01_Recurrent Neural Networks

01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.zh-CN.vtt

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09. Regra da cadeia-YAhIBOnbt54.zh-CN.vtt

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.en.vtt

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.pt-BR.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.zh-CN.vtt

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.zh-CN.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.en.vtt

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.en.vtt

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04. 03 RNN Applications V3 Final-6JbTNARuKII.zh-CN.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.pt-BR.vtt

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04. 03 RNN Applications V3 Final-6JbTNARuKII.en.vtt

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.pt-BR.vtt

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.zh-CN.vtt

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04. 03 RNN Applications V3 Final-6JbTNARuKII.pt-BR.vtt

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.en.vtt

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03. 02 RNN History V4 Final-HbxAnYUfRnc.zh-CN.vtt

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.zh-CN.vtt

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.pt-BR.vtt

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.zh-CN.vtt

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.zh-CN.vtt

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.en.vtt

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05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.zh-CN.vtt

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.zh-CN.vtt

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03. 02 RNN History V4 Final-HbxAnYUfRnc.pt-BR.vtt

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.pt-BR.vtt

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.en.vtt

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.en.vtt

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.en.vtt

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.zh-CN.vtt

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05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.en.vtt

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.pt-BR.vtt

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13. 16 RNN B V4 Final-wsif3p5t7CI.zh-CN.vtt

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.zh-CN.vtt

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.pt-BR.vtt

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24. RNN Summary-nXP0oGGRrO8.zh-CN.vtt

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.zh-CN.vtt

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.en.vtt

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12. 14 RNN A V4 Final-ofbnDxGSUcg.zh-CN.vtt

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.en.vtt

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13. 16 RNN B V4 Final-wsif3p5t7CI.en.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.zh-CN.vtt

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.pt-BR.vtt

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index.html

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24. RNN Summary-nXP0oGGRrO8.en.vtt

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.en.vtt

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12. 14 RNN A V4 Final-ofbnDxGSUcg.en.vtt

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.pt-BR.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.en.vtt

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12. 14 RNN A V4 Final-ofbnDxGSUcg.pt-BR.vtt

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13. 16 RNN B V4 Final-wsif3p5t7CI.pt-BR.vtt

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.pt-BR.vtt

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24. RNN Summary-nXP0oGGRrO8.pt-BR.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.pt-BR.vtt

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06. 07 FeedForward B V3-kTYbTVh1d0k.zh-CN.vtt

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.zh-CN.vtt

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08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.zh-CN.vtt

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01. Introducing Ortal .html

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14. RNN- Unfolded Model.html

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26. Wrap Up.html

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06. 07 FeedForward B V3-kTYbTVh1d0k.en.vtt

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16. RNN- Example.html

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06. 07 FeedForward B V3-kTYbTVh1d0k.pt-BR.vtt

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02. RNN Introduction.html

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15. Unfolded Model Quiz.html

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04. RNN Applications.html

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25. From RNN to LSTM.html

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09. Regra da cadeia-YAhIBOnbt54.mp4

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12. 14 RNN A V4 Final-ofbnDxGSUcg.mp4

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.mp4

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.mp4

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.mp4

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.mp4

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.mp4

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04. 03 RNN Applications V3 Final-6JbTNARuKII.mp4

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.mp4

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.mp4

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05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.mp4

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.mp4

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13. 16 RNN B V4 Final-wsif3p5t7CI.mp4

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.mp4

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.mp4

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03. 02 RNN History V4 Final-HbxAnYUfRnc.mp4

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.mp4

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06. 07 FeedForward B V3-kTYbTVh1d0k.mp4

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08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.mp4

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.mp4

36.16 MB

10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.mp4

37.06 MB

Part 07-Module 01-Lesson 02_Implementing Gradient Descent

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06. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt

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09. Further Reading.html

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04. Gradient Descent The Code.html

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02. Gradient Descent.html

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07. Backpropagation.html

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06. Multilayer Perceptrons.html

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08. Implementing Backpropagation.html

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05. Implementing Gradient Descent.html

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02. Gradient Descent-29PmNG7fuuM.mp4

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06. Multilayer perceptrons-Rs9petvTBLk.mp4

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07. Backpropagation-MZL97-2joxQ.mp4

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03. Gradient Descent-Math-7sxA5Ap8AWM.mp4

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Part 08-Module 01-Lesson 06_Transfer Learning in TensorFlow

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07. Building The Classifier-pPHiVddBY0Q.zh-CN.vtt

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04. Pretrained VGGNet-BpzI6Svmuv8.zh-CN.vtt

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08. Building The Classifier-6ifxRQ_gL7w.en.vtt

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10. Training And Testing-NLPtmQjGYCA.zh-CN.vtt

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03. VGGNet.html

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05. Data Preparation.html

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04. VGGNet Solution.html

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06. Data Preparation Solution.html

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09. Training.html

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07. Classifier.html

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02. Transfer Learning with VGGNet.html

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08. Classifier Solution.html

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03. Building VGGNet-615SslQiGvo.pt-BR.vtt

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05. Data Preparation-WfsDMq-b3y4.zh-CN.vtt

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02. Transfer Learning--WmQwYr0DYjY.zh-CN.vtt

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02. Transfer Learning--WmQwYr0DYjY.en.vtt

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05. Data Preparation-WfsDMq-b3y4.en.vtt

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01. Transfer Learning Intro.html

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03. Building VGGNet-615SslQiGvo.en.vtt

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07. Building The Classifier-pPHiVddBY0Q.mp4

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04. Pretrained VGGNet-BpzI6Svmuv8.mp4

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08. Building The Classifier-6ifxRQ_gL7w.mp4

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09. Training The Classifier-b7Fy3cIoJ1Y.mp4

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06. Data Preparation-WEtKkHlhhZA.mp4

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10. Training And Testing-NLPtmQjGYCA.mp4

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02. Transfer Learning--WmQwYr0DYjY.mp4

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03. Building VGGNet-615SslQiGvo.mp4

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05. Data Preparation-WfsDMq-b3y4.mp4

9.23 MB

Part 03-Module 05-Lesson 01_Transfer Learning in TensorFlow

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07. Building The Classifier-pPHiVddBY0Q.zh-CN.vtt

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04. Pretrained VGGNet-BpzI6Svmuv8.zh-CN.vtt

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08. Building The Classifier-6ifxRQ_gL7w.en.vtt

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10. Training And Testing-NLPtmQjGYCA.zh-CN.vtt

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03. VGGNet.html

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09. Training.html

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07. Classifier.html

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08. Classifier Solution.html

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05. Data Preparation-WfsDMq-b3y4.zh-CN.vtt

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02. Transfer Learning--WmQwYr0DYjY.zh-CN.vtt

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02. Transfer Learning--WmQwYr0DYjY.en.vtt

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05. Data Preparation-WfsDMq-b3y4.en.vtt

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01. Transfer Learning Intro.html

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03. Building VGGNet-615SslQiGvo.en.vtt

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07. Building The Classifier-pPHiVddBY0Q.mp4

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04. Pretrained VGGNet-BpzI6Svmuv8.mp4

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08. Building The Classifier-6ifxRQ_gL7w.mp4

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09. Training The Classifier-b7Fy3cIoJ1Y.mp4

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06. Data Preparation-WEtKkHlhhZA.mp4

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10. Training And Testing-NLPtmQjGYCA.mp4

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02. Transfer Learning--WmQwYr0DYjY.mp4

8.28 MB

03. Building VGGNet-615SslQiGvo.mp4

8.87 MB

05. Data Preparation-WfsDMq-b3y4.mp4

9.23 MB

Part 09-Module 01-Lesson 04_Hyperparameters

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02. Introduction-erwnzFD7AeE.zh-CN.vtt

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07. Number Of Hidden Units Layers-IkGAIQH5wH8.zh-CN.vtt

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08. RNN Hyperparameters-yQvnv7l_aUo.zh-CN.vtt

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05. Minibatch Size-GrrO1NFxaW8.zh-CN.vtt

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02. Introduction.html

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03. Learning Rate.html

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04. Learning Rate.html

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03. Learning Rate-HLMjeDez7ps.zh-CN.vtt

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07. Number Of Hidden Units Layers-IkGAIQH5wH8.mp4

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05. Minibatch Size-GrrO1NFxaW8.mp4

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03. Learning Rate-HLMjeDez7ps.mp4

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Part 04-Module 02-Lesson 02_Hyperparameters

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03. Learning Rate-HLMjeDez7ps.zh-CN.vtt

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07. Number Of Hidden Units Layers-IkGAIQH5wH8.mp4

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08. RNN Hyperparameters-yQvnv7l_aUo.mp4

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03. Learning Rate-HLMjeDez7ps.mp4

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Part 03-Module 01-Lesson 03_Hyperparameters

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07. Number Of Hidden Units Layers-IkGAIQH5wH8.mp4

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Part 02-Module 02-Lesson 03_Preparing for Siraj's Lesson

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01. Introduction.html

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05. RNNs and LSTMs.html

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Part 09-Module 01-Lesson 06_Sentiment Prediction RNN

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07. Solutions.html

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06. Training The Network-nknJ3Xu3ld0.mp4

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02. Sentiment Prediction-uGN3rZJRiMY.mp4

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07. Sentiment RNN 2-V9YGGjmoHS0.mp4

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Part 03-Module 04-Lesson 03_Sentiment Prediction RNN

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07. Solutions.html

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06. Training The Network-nknJ3Xu3ld0.pt-BR.vtt

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02. Sentiment Prediction-uGN3rZJRiMY.zh-CN.vtt

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02. Sentiment Prediction-uGN3rZJRiMY.en.vtt

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05. Building The RNN 1-XTD6slf64fM.zh-CN.vtt

12.07 KB

05. Building The RNN 1-XTD6slf64fM.pt-BR.vtt

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05. Building The RNN 1-XTD6slf64fM.en.vtt

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07. Sentiment RNN 2-V9YGGjmoHS0.zh-CN.vtt

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07. Sentiment RNN 2-V9YGGjmoHS0.pt-BR.vtt

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07. Sentiment RNN 2-V9YGGjmoHS0.en.vtt

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04. Creating Testing Sets-BRBbrNLz1ho.mp4

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03. Data Preprocessing-h4-LwZU9_k8.mp4

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06. Training The Network-nknJ3Xu3ld0.mp4

8.41 MB

02. Sentiment Prediction-uGN3rZJRiMY.mp4

11.25 MB

05. Building The RNN 1-XTD6slf64fM.mp4

19.11 MB

07. Sentiment RNN 2-V9YGGjmoHS0.mp4

23.11 MB

Part 02-Module 01-Lesson 01_Model Evaluation and Validation

04. Accuracy Question-AmFoZTf-Hb0.en.vtt

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08. K Fold Cross Validation-dRtgSJgSt_I.zh-CN.vtt

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08. K Fold Cross Validation-dRtgSJgSt_I.pt-BR.vtt

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08. K Fold Cross Validation-dRtgSJgSt_I.en-US.vtt

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07. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt

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07. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt

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07. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt

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05. Regression-Metrics-906P4BPnl9A.zh-CN.vtt

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05. Regression-Metrics-906P4BPnl9A.pt-BR.vtt

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index.html

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05. Regression-Metrics-906P4BPnl9A.en-US.vtt

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03. Confusion Matrix-Question-9GLNjmMUB_4.pt-BR.vtt

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03. Confusion Matrix-Question-9GLNjmMUB_4.zh-CN.vtt

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07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.zh-CN.vtt

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02. Testing.html

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05. Regression Metrics.html

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06. Types of Errors.html

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08. K-Fold Cross Validation.html

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03. Confusion Matrix-Question-9GLNjmMUB_4.en-US.vtt

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03. Confusion Matrix-Question-9GLNjmMUB_4.en.vtt

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07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.pt-BR.vtt

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04. Accuracy.html

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06. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt

6.02 KB

01. Introduction.html

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07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt

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06. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt

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03. Confusion Matrix.html

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02. Testing-gmxGRJSKEb0.zh-CN.vtt

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06. 04 L Types Of Errors-Twf1qnPZeSY.en-US.vtt

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07. Model Complexity Graph.html

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02. Testing-gmxGRJSKEb0.pt-BR.vtt

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02. Testing-gmxGRJSKEb0.en-US.vtt

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img

complexity.png

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accuracy.png

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confusion.png

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meme.png

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04. Accuracy Question-AmFoZTf-Hb0.mp4

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08. K Fold Cross Validation-dRtgSJgSt_I.mp4

2.79 MB

05. Regression-Metrics-906P4BPnl9A.mp4

3.35 MB

03. Confusion Matrix-Question-9GLNjmMUB_4.mp4

5.04 MB

07. Model Complexity Graph-Question-YS5OQCA5cLY.mp4

5.41 MB

02. Testing-gmxGRJSKEb0.mp4

5.63 MB

06. 04 L Types Of Errors-Twf1qnPZeSY.mp4

6.55 MB

07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4

9.23 MB

Part 02-Module 05-Lesson 04_Image Classification

01. Project Intro-awEYy2Df3hg.zh-CN.vtt

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01. Project Intro-awEYy2Df3hg.en.vtt

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01. Project Intro-awEYy2Df3hg.pt-BR.vtt

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index.html

3.72 KB

01. Introduction to the Project.html

4.96 KB

Project Description - Image Classification.html

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Project Rubric - Image Classification.html

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01. Project Intro-awEYy2Df3hg.mp4

2.89 MB

Part 11-Module 01-Lesson 01_Introduction to RL

05. Resources-_YPqfAnCqtk.zh-CN.vtt

1.83 KB

04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt

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05. Resources-_YPqfAnCqtk.en.vtt

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01. Introduction-6jSFl5kxIBs.zh-CN.vtt

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04. OpenAI Gym-MktEOWp3QLg.en.vtt

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05. Resources-_YPqfAnCqtk.pt-BR.vtt

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01. Introduction-6jSFl5kxIBs.en.vtt

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01. Introduction-6jSFl5kxIBs.pt-BR.vtt

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04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt

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02. Applications-CV6B84mKRNM.zh-CN.vtt

2.91 KB

02. Applications-CV6B84mKRNM.en.vtt

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02. Applications-CV6B84mKRNM.pt-BR.vtt

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index.html

3.78 KB

03. The Setting.html

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01. Introduction.html

5.43 KB

06. Reference Guide.html

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05. Resources.html

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03. The Setting-nh8Gwdu19nc.zh-CN.vtt

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04. OpenAI Gym.html

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03. The Setting-nh8Gwdu19nc.en.vtt

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02. Applications.html

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03. The Setting-nh8Gwdu19nc.pt-BR.vtt

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img

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01. Introduction-6jSFl5kxIBs.mp4

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05. Resources-_YPqfAnCqtk.mp4

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03. The Setting-nh8Gwdu19nc.mp4

7.75 MB

02. Applications-CV6B84mKRNM.mp4

8.46 MB

04. OpenAI Gym-MktEOWp3QLg.mp4

9.47 MB

Part 03-Module 01-Lesson 01_Intro to Recurrent Neural Networks

04. Sequence-Batching-Z4OiyU0Cldg.zh-CN.vtt

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04. Sequence-Batching-Z4OiyU0Cldg.en.vtt

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04. Sequence-Batching-Z4OiyU0Cldg.pt-BR.vtt

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11. Network Loss-itu-uNK4brc.zh-CN.vtt

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09. LSTM Cell Solution-X4uA0dq_4jA.zh-CN.vtt

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03. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt

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12. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt

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09. LSTM Cell Solution-X4uA0dq_4jA.pt-BR.vtt

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11. Network Loss-itu-uNK4brc.en.vtt

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09. LSTM Cell Solution-X4uA0dq_4jA.en.vtt

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11. Network Loss-itu-uNK4brc.pt-BR.vtt

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12. Output And Loss Solutions-CT8hcU7FmGc.pt-BR.vtt

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03. Character-Wise RNN-dXl3eWCGLdU.en.vtt

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13. Build The Network-RVNjDlWTBQU.zh-CN.vtt

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12. Output And Loss Solutions-CT8hcU7FmGc.en.vtt

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03. Character-Wise RNN-dXl3eWCGLdU.pt-BR.vtt

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07. Batching Data Solution-o3nBxHJLQcc.zh-CN.vtt

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01. Intro To RNNs-64HSG6HAfEI.zh-CN.vtt

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13. Build The Network-RVNjDlWTBQU.pt-BR.vtt

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13. Build The Network-RVNjDlWTBQU.en.vtt

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07. Batching Data Solution-o3nBxHJLQcc.en.vtt

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07. Batching Data Solution-o3nBxHJLQcc.pt-BR.vtt

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01. Intro To RNNs-64HSG6HAfEI.pt.vtt

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index.html

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10. RNN Output-RkanDkcrTxs.zh-CN.vtt

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08. LSTM Cell-ajC-5uWB8S4.zh-CN.vtt

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10. RNN Output-RkanDkcrTxs.pt-BR.vtt

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10. RNN Output-RkanDkcrTxs.en.vtt

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02. LSTMs.html

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08. LSTM Cell.html

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10. RNN Output.html

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11. Network Loss.html

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08. LSTM Cell-ajC-5uWB8S4.en.vtt

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04. Sequence Batching.html

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13. Build the Network.html

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09. LSTM Cell Solution.html

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03. Character-wise RNNs.html

6.09 KB

07. Batching Data Solution.html

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12. Output and Loss Solutions.html

6.13 KB

14. Build the Network Solution.html

6.15 KB

06. Implementing a Character-wise RNN.html

6.19 KB

08. LSTM Cell-ajC-5uWB8S4.pt-BR.vtt

6.21 KB

01. Intro to RNNs.html

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15. RNN Resources.html

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05. Character-wise RNN Notebook.html

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02. LSTMs-RYbSHogZetc.zh-CN.vtt

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14. Build The Network And Results-hu8iMMqajmQ.zh-CN.vtt

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02. LSTMs-RYbSHogZetc.en.vtt

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02. LSTMs-RYbSHogZetc.pt.vtt

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14. Build The Network And Results-hu8iMMqajmQ.pt-BR.vtt

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14. Build The Network And Results-hu8iMMqajmQ.en.vtt

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06. Implementing a Character-wise RNN-KPCMn_jg2oY.zh-CN.vtt

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06. Implementing a Character-wise RNN-KPCMn_jg2oY.en.vtt

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06. Implementing a Character-wise RNN-KPCMn_jg2oY.pt-BR.vtt

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04. Sequence-Batching-Z4OiyU0Cldg.mp4

2.29 MB

03. Character-Wise RNN-dXl3eWCGLdU.mp4

2.88 MB

09. LSTM Cell Solution-X4uA0dq_4jA.mp4

3.55 MB

01. Intro To RNNs-64HSG6HAfEI.mp4

3.99 MB

11. Network Loss-itu-uNK4brc.mp4

4.28 MB

12. Output And Loss Solutions-CT8hcU7FmGc.mp4

4.69 MB

07. Batching Data Solution-o3nBxHJLQcc.mp4

5.08 MB

02. LSTMs-RYbSHogZetc.mp4

6.87 MB

13. Build The Network-RVNjDlWTBQU.mp4

7.10 MB

08. LSTM Cell-ajC-5uWB8S4.mp4

7.79 MB

10. RNN Output-RkanDkcrTxs.mp4

8.92 MB

14. Build The Network And Results-hu8iMMqajmQ.mp4

13.27 MB

06. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4

13.63 MB

Part 09-Module 01-Lesson 03_Implementation of RNN and LSTM

03. Sequence-Batching-Z4OiyU0Cldg.zh-CN.vtt

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10. Network Loss-itu-uNK4brc.zh-CN.vtt

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08. LSTM Cell Solution-X4uA0dq_4jA.zh-CN.vtt

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02. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt

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08. LSTM Cell Solution-X4uA0dq_4jA.pt-BR.vtt

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10. Network Loss-itu-uNK4brc.en.vtt

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08. LSTM Cell Solution-X4uA0dq_4jA.en.vtt

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10. Network Loss-itu-uNK4brc.pt-BR.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.pt-BR.vtt

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02. Character-Wise RNN-dXl3eWCGLdU.en.vtt

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12. Build The Network-RVNjDlWTBQU.zh-CN.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.en.vtt

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02. Character-Wise RNN-dXl3eWCGLdU.pt-BR.vtt

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06. Batching Data Solution-o3nBxHJLQcc.zh-CN.vtt

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12. Build The Network-RVNjDlWTBQU.pt-BR.vtt

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06. Batching Data Solution-o3nBxHJLQcc.en.vtt

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06. Batching Data Solution-o3nBxHJLQcc.pt-BR.vtt

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09. RNN Output-RkanDkcrTxs.zh-CN.vtt

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07. LSTM Cell-ajC-5uWB8S4.zh-CN.vtt

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07. LSTM Cell.html

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09. RNN Output.html

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10. Network Loss.html

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03. Sequence Batching.html

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12. Build the Network.html

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08. LSTM Cell Solution.html

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02. Character-wise RNNs.html

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06. Batching Data Solution.html

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11. Output and Loss Solutions.html

5.95 KB

13. Build the Network Solution.html

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01. Intro.html

5.99 KB

05. Implementing a Character-wise RNN.html

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07. LSTM Cell-ajC-5uWB8S4.en.vtt

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04. Character-wise RNN Notebook.html

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13. Build The Network And Results-hu8iMMqajmQ.zh-CN.vtt

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.zh-CN.vtt

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.en.vtt

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.pt-BR.vtt

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03. Sequence-Batching-Z4OiyU0Cldg.mp4

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02. Character-Wise RNN-dXl3eWCGLdU.mp4

2.88 MB

08. LSTM Cell Solution-X4uA0dq_4jA.mp4

3.55 MB

10. Network Loss-itu-uNK4brc.mp4

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11. Output And Loss Solutions-CT8hcU7FmGc.mp4

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06. Batching Data Solution-o3nBxHJLQcc.mp4

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12. Build The Network-RVNjDlWTBQU.mp4

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07. LSTM Cell-ajC-5uWB8S4.mp4

7.79 MB

09. RNN Output-RkanDkcrTxs.mp4

8.92 MB

13. Build The Network And Results-hu8iMMqajmQ.mp4

13.27 MB

05. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4

13.63 MB

Part 01-Module 03-Lesson 03_Your first neural network

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01. Introduction to the Project.html

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Project Rubric - Your first neural network.html

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Project Description - Your first neural network.html

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media

Screen+Shot+2017-01-27+at+11.38.54+AM.png

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01. Introduction to the Project-dOwEDeJp8yw.mp4

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Part 08-Module 01-Lesson 05_Autoencoders

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index.html

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03. A Simple Autoencoder.html

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02. Autoencoders.html

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05. Convolutional Autoencoders.html

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04. Simple Autoencoder Solution.html

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06. Convolutional Autoencoders Solution.html

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04. Simple Autoencoder Solution-Nv_D6DHfEk8.zh-CN.vtt

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04. Simple Autoencoder Solution-Nv_D6DHfEk8.pt-BR.vtt

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01. Autoencoder Lesson Intro.html

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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.zh-CN.vtt

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04. Simple Autoencoder Solution-Nv_D6DHfEk8.en.vtt

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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.pt-BR.vtt

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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.en.vtt

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05. Convolutional Autoencoders-18SZVRaumGs.zh-CN.vtt

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03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.en.vtt

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05. Convolutional Autoencoders-18SZVRaumGs.pt-BR.vtt

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02. Autoencoders-ar5Iyx68cWc.mp4

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04. Simple Autoencoder Solution-Nv_D6DHfEk8.mp4

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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.mp4

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03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.mp4

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05. Convolutional Autoencoders-18SZVRaumGs.mp4

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Part 03-Module 08-Lesson 02_Autoencoders

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03. A Simple Autoencoder.html

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05. Convolutional Autoencoders.html

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01. Autoencoder Lesson Intro.html

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02. Autoencoders-ar5Iyx68cWc.mp4

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04. Simple Autoencoder Solution-Nv_D6DHfEk8.mp4

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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.mp4

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03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.mp4

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Part 03-Module 02-Lesson 01_Embeddings and Word2vec

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06. Building The Network-fhSb5c6UX6M.pt-BR.vtt

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04. Making Batches.html

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05. Batches Solution.html

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09. Training Results.html

5.48 KB

07. Negative Sampling.html

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06. Building the Network.html

5.51 KB

03. Subsampling Solution.html

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02. Implementing Word2Vec.html

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08. Building the Network Solution.html

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03. Subsampling Solution-MAUM_mV_lj8.en.vtt

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01. Embeddings Intro.html

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02. Implementing Word2Vec-7M431_f9HgE.zh-CN.vtt

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02. Implementing Word2Vec-7M431_f9HgE.en.vtt

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07. Negative Sampling-gW17AHBKbHY.mp4

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08. Building The Network Solution-pkBAhQ2Ki-8.mp4

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04. Making Batches-jx7qwdw-94k.mp4

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06. Building The Network-fhSb5c6UX6M.mp4

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03. Subsampling Solution-MAUM_mV_lj8.mp4

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09. Training Results-uISA5ns47s8.mp4

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02. Implementing Word2Vec-7M431_f9HgE.mp4

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Part 09-Module 01-Lesson 05_Embeddings and Word2vec

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09. Training Results-uISA5ns47s8.zh-CN.vtt

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08. Building The Network Solution-pkBAhQ2Ki-8.pt-BR.vtt

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08. Building The Network Solution-pkBAhQ2Ki-8.en.vtt

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06. Building The Network-fhSb5c6UX6M.zh-CN.vtt

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09. Training Results-uISA5ns47s8.en.vtt

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09. Training Results-uISA5ns47s8.pt-BR.vtt

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04. Making Batches-jx7qwdw-94k.en.vtt

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06. Building The Network-fhSb5c6UX6M.pt-BR.vtt

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03. Subsampling Solution-MAUM_mV_lj8.zh-CN.vtt

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06. Building The Network-fhSb5c6UX6M.en.vtt

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04. Making Batches.html

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05. Batches Solution.html

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09. Training Results.html

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07. Negative Sampling.html

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06. Building the Network.html

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03. Subsampling Solution.html

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02. Implementing Word2Vec.html

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08. Building the Network Solution.html

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03. Subsampling Solution-MAUM_mV_lj8.en.vtt

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03. Subsampling Solution-MAUM_mV_lj8.pt-BR.vtt

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01. Embeddings Intro.html

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02. Implementing Word2Vec-7M431_f9HgE.zh-CN.vtt

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02. Implementing Word2Vec-7M431_f9HgE.en.vtt

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07. Negative Sampling-gW17AHBKbHY.mp4

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08. Building The Network Solution-pkBAhQ2Ki-8.mp4

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04. Making Batches-jx7qwdw-94k.mp4

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06. Building The Network-fhSb5c6UX6M.mp4

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03. Subsampling Solution-MAUM_mV_lj8.mp4

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09. Training Results-uISA5ns47s8.mp4

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02. Implementing Word2Vec-7M431_f9HgE.mp4

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Part 06-Module 01-Lesson 04_Jupyter Notebooks

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02. Jupyter-qiYDWFLyXvg.zh-CN.vtt

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02. Jupyter-qiYDWFLyXvg.ar.vtt

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index.html

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12. Finishing up.html

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03. Installing Jupyter Notebook.html

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01. Instructor.html

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08. Keyboard shortcuts.html

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06. Code cells.html

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10. Converting notebooks.html

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11. Creating a slideshow.html

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05. Notebook interface.html

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07. Markdown cells.html

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09. Magic keywords.html

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04. Launching the notebook server.html

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02. What are Jupyter notebooks.html

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02. Jupyter-qiYDWFLyXvg.mp4

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Part 01-Module 01-Lesson 03_Jupyter Notebooks

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02. Jupyter-qiYDWFLyXvg.zh-CN.vtt

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02. Jupyter-qiYDWFLyXvg.ar.vtt

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index.html

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12. Finishing up.html

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03. Installing Jupyter Notebook.html

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01. Instructor.html

5.87 KB

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06. Code cells.html

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10. Converting notebooks.html

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05. Notebook interface.html

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07. Markdown cells.html

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09. Magic keywords.html

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04. Launching the notebook server.html

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02. What are Jupyter notebooks.html

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nbconvert-example.png

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notebook+interface.mp4

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02. Jupyter-qiYDWFLyXvg.mp4

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Part 03-Module 06-Lesson 01_Sequence to Sequence

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05. Architecture in More Depth-rdAo4MqLbEk.zh-CN.vtt

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04. Architecture encoder decoder-dkHdEAJnV_w.zh-CN.vtt

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05. Architecture in More Depth-rdAo4MqLbEk.pt-BR.vtt

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02. Jay's Introduction-HPOzAlXhuxQ.zh-CN.vtt

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05. Architecture in More Depth-rdAo4MqLbEk.en.vtt

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04. Architecture encoder decoder-dkHdEAJnV_w.en.vtt

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02. Jay's Introduction-HPOzAlXhuxQ.pt-BR.vtt

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03. Applications.html

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02. Jay Introduction.html

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04. Architectures.html

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05. Architectures in More Depth.html

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02. Jay's Introduction-HPOzAlXhuxQ.en.vtt

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10. Sequence to Sequence in TensorFlow.html

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09. Further Reading.html

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06. Preprocessing.html

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01. Introducing Jay Alammar.html

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07. Sequence to sequence in TensorFlow.html

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03. Applications seq2seq-tDJBDwriJYQ.mp4

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04. Architecture encoder decoder-dkHdEAJnV_w.mp4

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05. Architecture in More Depth-rdAo4MqLbEk.mp4

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02. Jay's Introduction-HPOzAlXhuxQ.mp4

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06. Preprocessing-ktQW6p9pOS4.mp4

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Part 03-Module 07-Lesson 01_Reinforcement Learning

02. 01 Q-Learning-Npu9gyD6-4o.zh-CN.vtt

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03. Q-Learning.html

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02. Reinforcement Learning.html

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04. Deep Q-Learning.html

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02. 01 Q-Learning-Npu9gyD6-4o.mp4

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03. 02 Q-Learning-WQgdnzzhSLM.mp4

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Part 10-Module 01-Lesson 02_Deep Convolutional GANs

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08. Discriminator Solution-ffPWI2yJscw.en.vtt

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10. Hyperparameters Solution-Rt8MlVDtpi8.pt-BR.vtt

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06. Generator Solution-jyPwUEZg05Q.en.vtt

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10. Hyperparameters Solution-Rt8MlVDtpi8.en.vtt

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09. Building And Training The Network-nXKk9GI4X14.pt-BR.vtt

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07. Discriminator.html

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02. DCGAN Architecture.html

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06. Generator Solution.html

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08. Discriminator Solution.html

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05. DCGAN and the Generator.html

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10. Hyperparameter Solutions.html

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09. Building and Training the Network.html

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04. DCGAN Implementation.html

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02. Deconvolution-sX_AxtB6CHI.zh-CN.vtt

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03. Batch Normalization.html

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01. Deep Convolutional GANs.html

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02. Deconvolution-sX_AxtB6CHI.pt-BR.vtt

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02. Deconvolution-sX_AxtB6CHI.en.vtt

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05. DCGAN And The Generator-CH6BxLTKt7s.pt-BR.vtt

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05. DCGAN And The Generator-CH6BxLTKt7s.zh-CN.vtt

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05. DCGAN And The Generator-CH6BxLTKt7s.en.vtt

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07. Discriminator-XRqOUbf96eI.mp4

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08. Discriminator Solution-ffPWI2yJscw.mp4

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10. Hyperparameters Solution-Rt8MlVDtpi8.mp4

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06. Generator Solution-jyPwUEZg05Q.mp4

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09. Building And Training The Network-nXKk9GI4X14.mp4

8.08 MB

02. Deconvolution-sX_AxtB6CHI.mp4

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05. DCGAN And The Generator-CH6BxLTKt7s.mp4

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Part 04-Module 02-Lesson 03_Deep Convolutional GANs

07. Discriminator-XRqOUbf96eI.pt-BR.vtt

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06. Generator Solution-jyPwUEZg05Q.en.vtt

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10. Hyperparameters Solution-Rt8MlVDtpi8.en.vtt

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index.html

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09. Building And Training The Network-nXKk9GI4X14.pt-BR.vtt

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09. Building And Training The Network-nXKk9GI4X14.zh-CN.vtt

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07. Discriminator.html

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02. DCGAN Architecture.html

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06. Generator Solution.html

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08. Discriminator Solution.html

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05. DCGAN and the Generator.html

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10. Hyperparameter Solutions.html

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09. Building and Training the Network.html

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09. Building And Training The Network-nXKk9GI4X14.en.vtt

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04. DCGAN Implementation.html

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02. Deconvolution-sX_AxtB6CHI.zh-CN.vtt

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03. Batch Normalization.html

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01. Deep Convolutional GANs.html

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02. Deconvolution-sX_AxtB6CHI.pt-BR.vtt

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02. Deconvolution-sX_AxtB6CHI.en.vtt

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05. DCGAN And The Generator-CH6BxLTKt7s.pt-BR.vtt

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05. DCGAN And The Generator-CH6BxLTKt7s.zh-CN.vtt

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07. Discriminator-XRqOUbf96eI.mp4

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08. Discriminator Solution-ffPWI2yJscw.mp4

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10. Hyperparameters Solution-Rt8MlVDtpi8.mp4

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06. Generator Solution-jyPwUEZg05Q.mp4

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09. Building And Training The Network-nXKk9GI4X14.mp4

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02. Deconvolution-sX_AxtB6CHI.mp4

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05. DCGAN And The Generator-CH6BxLTKt7s.mp4

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Part 04-Module 02-Lesson 05_Semi-Supervised Learning

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12. Trained Semi-Supervised GAN-9yWYZDX8-O8.zh-CN.vtt

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04. Data Prep.html

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10. Model Loss Solution-r3DtohmychE.pt-BR.vtt

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03. Introducing Semi-Supervised Learning-tnCClNy5z5c.mp4

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04. Data Prep-P5hOx09mwaM.mp4

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12. Trained Semi-Supervised GAN-9yWYZDX8-O8.mp4

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09. Discriminator Solution-_X8ssUzu_Bo.mp4

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10. Model Loss Solution-r3DtohmychE.mp4

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05. Building The Generator And Discriminator-OWytckbbeGQ.mp4

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Part 10-Module 01-Lesson 03_Semi-Supervised Learning

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03. Introducing Semi-Supervised Learning.html

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05. Building The Generator And Discriminator.html

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05. Building The Generator And Discriminator-OWytckbbeGQ.zh-CN.vtt

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06. Model Loss Exercise-W7TawMNxBds.pt-BR.vtt

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11. Model Optimizer Solution-_Qhz9SbR7xY.mp4

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08. Training The Network -P-LXQPVXl4A.mp4

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03. Introducing Semi-Supervised Learning-tnCClNy5z5c.mp4

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04. Data Prep-P5hOx09mwaM.mp4

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12. Trained Semi-Supervised GAN-9yWYZDX8-O8.mp4

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09. Discriminator Solution-_X8ssUzu_Bo.mp4

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10. Model Loss Solution-r3DtohmychE.mp4

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02. Semi-Supervised Learning-_LRpHPxZaX0.mp4

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07. Model Optimization Exercise-wNpI1wUA4Io.mp4

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06. Model Loss Exercise-W7TawMNxBds.mp4

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05. Building The Generator And Discriminator-OWytckbbeGQ.mp4

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Part 06-Module 01-Lesson 03_Anaconda

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02. Why Anaconda-VXukXZv7SCQ.en.vtt

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index.html

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02. Why Anaconda-VXukXZv7SCQ.ar.vtt

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01. Instructor.html

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02. Introduction.html

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08. Best practices.html

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09. On Python versions at Udacity.html

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07. More environment actions.html

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04. Installing Anaconda.html

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05. Managing packages.html

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06. Managing environments.html

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03. What is Anaconda.html

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img

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media

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02. Why Anaconda-VXukXZv7SCQ.mp4

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Part 01-Module 01-Lesson 02_Anaconda

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index.html

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02. Why Anaconda-VXukXZv7SCQ.ar.vtt

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01. Instructor.html

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02. Introduction.html

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08. Best practices.html

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09. On Python versions at Udacity.html

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05. Managing packages.html

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06. Managing environments.html

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03. What is Anaconda.html

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img

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media

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conda_install.mp4

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conda_default_install.mp4

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02. Why Anaconda-VXukXZv7SCQ.mp4

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Part 03-Module 06-Lesson 02_Siraj's Chatbot

index.html

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01. How to Make a Chatbot.html

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01. How to Make a Chatbot - Intro to Deep Learning #12-t5qgjJIBy9g.en.vtt

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01. How to Make a Chatbot - Intro to Deep Learning #12-t5qgjJIBy9g.mp4

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Part 03-Module 02-Lesson 03_Q&A with FloydHub Founders

index.html

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01. FloydHub QA.html

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01. Floyd QA-KUc59DPfBeo.pt-BR.vtt

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01. Floyd QA-KUc59DPfBeo.en-US.vtt

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01. Floyd QA-KUc59DPfBeo.mp4

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Part 03-Module 02-Lesson 02_Siraj's Style Transfer

index.html

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01. How to Generate Art.html

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01. How to Generate Art - Intro to Deep Learning #8-Oex0eWoU7AQ.en.vtt

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01. How to Generate Art - Intro to Deep Learning #8-Oex0eWoU7AQ.mp4

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Part 04-Module 01-Lesson 02_Siraj's Video Generation

index.html

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01. How to Generate Video.html

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01. How to Generate Video - Intro to Deep Learning #15--E2N1kQc8MM.en.vtt

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01. How to Generate Video - Intro to Deep Learning #15--E2N1kQc8MM.mp4

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Part 11-Module 02-Lesson 01_Teach a Quadcopter How to Fly

index.html

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01. Project Description.html

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Part 03-Module 08-Lesson 01_Siraj's Image Generation

index.html

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01. How to Generate Images.html

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01. How to Generate Images - Intro to Deep Learning #14-3-UDwk1U77s.en.vtt

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01. How to Generate Images - Intro to Deep Learning #14-3-UDwk1U77s.mp4

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Part 08-Module 02-Lesson 01_CNN Project Dog Breed Classifier

index.html

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01. Project Description.html

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Part 03-Module 07-Lesson 02_Siraj's Reinforcement Learning

index.html

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01. How to Win Slot Machines.html

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01. How to Win Slot Machines - Intro to Deep Learning #13-AIeWLTUYLZQ.en.vtt

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01. How to Win Slot Machines - Intro to Deep Learning #13-AIeWLTUYLZQ.mp4

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Part 04-Module 02-Lesson 01_Siraj's One-Shot Learning

index.html

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01. How to Learn from Little Data.html

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01. How to Learn from Little Data - Intro to Deep Learning #17-tChcZpBbTTA.mp4

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Part 03-Module 04-Lesson 01_Siraj's Text Summarization

index.html

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01. How to Make a Text Summarizer.html

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01. How to Make a Text Summarizer - Intro to Deep Learning #10-ogrJaOIuBx4.mp4

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Part 03-Module 01-Lesson 02_Siraj's Stock Prediction

index.html

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01. How to Predict Stock Prices Easily.html

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01. How to Predict Stock Prices Easily - Intro to Deep Learning #7-ftMq5ps503w.mp4

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Part 03-Module 05-Lesson 02_Siraj's Language Translation

index.html

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01. How to Make a Language Translator.html

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01. How to Make a Language Translator - Intro to Deep Learning #11-nRBnh4qbPHI.en.vtt

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Part 05-Module 01-Lesson 01_Enroll in your next Nanodegree program

index.html

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img

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Part 08-Module 01-Lesson 03_Weight Initialization

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Part 03-Module 04-Lesson 02_Weight Initialization

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04. Too Small.html

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02. Ones and Zeros.html

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05. Normal Distribution.html

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03. Uniform Distribution.html

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06. Additional Material.html

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02. Weight Initialization 1-6vXMYu_TQIA.mp4

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03. Weight Initialization 2-BI3f0Cdc_nU.mp4

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Part 02-Module 05-Lesson 03_Siraj's Image Classification

index.html

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01. On Keras.html

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02. How to Make an Image Classifier.html

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02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.ru.vtt

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02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.mp4

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Part 11-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task

index.html

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03. Mini Project.html

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01. Introduction.html

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02. Instructions.html

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img

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open-agent-monitor-main.gif

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Part 03-Module 03-Lesson 02_Siraj's Music Generation

index.html

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02. How to Succeed in any Programming Interview.html

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01. How to Generate Music.html

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02. How to Succeed in any Programming Interview-5KB5KAak6tM.en.vtt

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01. How to Generate Music - Intro to Deep Learning #9-4DMm5Lhey1U.en.vtt

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02. How to Succeed in any Programming Interview-5KB5KAak6tM.ko.vtt

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02. How to Succeed in any Programming Interview-5KB5KAak6tM.mp4

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01. How to Generate Music - Intro to Deep Learning #9-4DMm5Lhey1U.mp4

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Part 06-Module 01-Lesson 02_Applying Deep Learning

index.html

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05. Books to Read.html

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04. Flappy Bird.html

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03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.mp4

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Part 01-Module 01-Lesson 04_Applying Deep Learning

index.html

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01. Introduction.html

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05. Books to Read.html

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03. DeepTraffic.html

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04. Flappy Bird.html

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assets

css

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fonts

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Part 03-Module 03-Lesson 01_TensorBoard

index.html

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04. Inspecting Variables.html

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05. Choosing Hyperparameters.html

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Part 08-Module 01-Lesson 01_Cloud Computing

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screen-shot-2017-11-26-at-9.38.24-am.png

440.90 KB

06. Login to the Instance.html

9.45 KB

05. Launch an Instance.html

12.53 KB

Part 02-Module 04-Lesson 01_Cloud Computing

index.html

3.92 KB

07. More Resources.html

5.39 KB

02. Create an AWS Account.html

5.89 KB

01. Overview.html

6.47 KB

04. Apply Credits.html

6.63 KB

03. Get Access to GPU Instances.html

8.57 KB

img

launch.png

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edit-security-group.png

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aws-create-account.png

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review-and-launch.png

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launch-instance.png

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screen-shot-2018-01-08-at-5.37.22-am.png

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aws-add-sec-group.png

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stop.png

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amazonwebservices-logo.svg.png

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p2xlarge-limit-request.png

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screen-shot-2018-07-19-at-5.39.37-pm.png

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screen-shot-2017-11-26-at-10.30.15-am.png

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p2-limit-increase.png

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screen-shot-2017-06-13-at-12.58.03-pm.png

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screen-shot-2018-01-08-at-5.38.03-am.png

276.13 KB

screen-shot-2017-11-26-at-9.55.20-am.png

414.22 KB

screen-shot-2017-11-26-at-9.38.24-am.png

440.90 KB

06. Login to the Instance.html

9.45 KB

05. Launch an Instance.html

12.53 KB

Part 01-Module 03-Lesson 01_Matrix Math and NumPy Refresher

04. Element-wise Matrix Operations-vjUykZyzko4.zh-CN.vtt

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04. Element-wise Matrix Operations-vjUykZyzko4.en.vtt

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04. Element-wise Matrix Operations-vjUykZyzko4.pt-BR.vtt

4.32 KB

index.html

4.33 KB

02. Data Dimensions.html

5.76 KB

09. Matrix Transposes.html

5.76 KB

04. Element-wise Matrix Operations.html

5.84 KB

01. Introduction.html

5.93 KB

06. Matrix Multiplication Part 1.html

6.06 KB

07. Matrix Multiplication Part 2.html

7.05 KB

02. Data Has Dimensions-F4NSv776X0c.zh-CN.vtt

7.59 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.zh-CN.vtt

7.72 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.pt-BR.vtt

7.91 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.en.vtt

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07. Matrix Multiplication Part 2-8jtk8BzBdj8.zh-CN.vtt

8.61 KB

02. Data Has Dimensions-F4NSv776X0c.en.vtt

8.66 KB

08. NumPy Matrix Multiplication.html

8.67 KB

02. Data Has Dimensions-F4NSv776X0c.pt-BR.vtt

8.80 KB

05. Element-wise Operations in NumPy.html

9.17 KB

10. Transposes in NumPy.html

9.18 KB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.pt-BR.vtt

9.31 KB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.en.vtt

9.88 KB

09. Matrix Transposes-NVK5xCY3CZE.zh-CN.vtt

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11. NumPy Quiz.html

10.47 KB

09. Matrix Transposes-NVK5xCY3CZE.pt-BR.vtt

11.08 KB

09. Matrix Transposes-NVK5xCY3CZE.en-US.vtt

11.81 KB

03. Data in NumPy.html

13.36 KB

img

input-times-weights.png

51.82 KB

04. Element-wise Matrix Operations-vjUykZyzko4.mp4

4.03 MB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.mp4

5.89 MB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.mp4

7.34 MB

09. Matrix Transposes-NVK5xCY3CZE.mp4

9.09 MB

02. Data Has Dimensions-F4NSv776X0c.mp4

10.61 MB

Part 06-Module 01-Lesson 05_Matrix Math and NumPy Refresher

04. Element-wise Matrix Operations-vjUykZyzko4.zh-CN.vtt

3.97 KB

04. Element-wise Matrix Operations-vjUykZyzko4.en.vtt

4.24 KB

04. Element-wise Matrix Operations-vjUykZyzko4.pt-BR.vtt

4.32 KB

index.html

4.33 KB

02. Data Dimensions.html

5.76 KB

09. Matrix Transposes.html

5.76 KB

04. Element-wise Matrix Operations.html

5.84 KB

01. Introduction.html

5.93 KB

06. Matrix Multiplication Part 1.html

6.06 KB

07. Matrix Multiplication Part 2.html

7.05 KB

02. Data Has Dimensions-F4NSv776X0c.zh-CN.vtt

7.59 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.zh-CN.vtt

7.72 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.pt-BR.vtt

7.91 KB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.en.vtt

7.95 KB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.zh-CN.vtt

8.61 KB

02. Data Has Dimensions-F4NSv776X0c.en.vtt

8.66 KB

08. NumPy Matrix Multiplication.html

8.67 KB

02. Data Has Dimensions-F4NSv776X0c.pt-BR.vtt

8.80 KB

05. Element-wise Operations in NumPy.html

9.17 KB

10. Transposes in NumPy.html

9.18 KB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.pt-BR.vtt

9.31 KB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.en.vtt

9.88 KB

09. Matrix Transposes-NVK5xCY3CZE.zh-CN.vtt

10.35 KB

11. NumPy Quiz.html

10.47 KB

09. Matrix Transposes-NVK5xCY3CZE.pt-BR.vtt

11.08 KB

09. Matrix Transposes-NVK5xCY3CZE.en-US.vtt

11.81 KB

03. Data in NumPy.html

13.36 KB

img

input-times-weights.png

51.82 KB

04. Element-wise Matrix Operations-vjUykZyzko4.mp4

4.03 MB

06. Matrix Multiplication Part 1-JRoCFQRP4B0.mp4

5.89 MB

07. Matrix Multiplication Part 2-8jtk8BzBdj8.mp4

7.34 MB

09. Matrix Transposes-NVK5xCY3CZE.mp4

9.09 MB

02. Data Has Dimensions-F4NSv776X0c.mp4

10.61 MB

Part 02-Module 02-Lesson 02_Intro to TFLearn

index.html

4.09 KB

img

softmax-math.png

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z93yz2vrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaecamayaacbngamaajamjaeaaegtxgaaakqjywaaan

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mnist-matrix.png

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softmax.png

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relu.png

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screen-shot-2017-02-02-at-10.00.16-pm.png

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softmax-input-output.png

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sigmoids.png

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cross-entropy-diagram.png

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mat-headshot.png

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cezanne-c-600x600.jpg

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05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.zh-CN.vtt

5.69 KB

05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.en.vtt

6.22 KB

01. Welcome to this lesson!.html

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05. Sentiment Analysis Solution.html

6.31 KB

05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.pt-BR.vtt

6.50 KB

07. Handwritten Digit Recognition Solution.html

6.74 KB

04. TFLearn-YF7S6hi4bnc.zh-CN.vtt

7.24 KB

04. TFLearn-YF7S6hi4bnc.en-US.vtt

7.93 KB

03. Categorical Cross-Entropy.html

8.58 KB

04. Sentiment Analysis with TFLearn.html

8.88 KB

06. Handwritten Digit Recognition.html

9.34 KB

02. ReLU and Softmax Activation Functions.html

14.29 KB

04. TFLearn-YF7S6hi4bnc.mp4

11.77 MB

05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.mp4

17.55 MB

Part 08-Module 01-Lesson 02_CNNs in TensorFlow

index.html

4.10 KB

08. CNNs - Additional Resources.html

5.96 KB

06. Solution Max Pooling Layers.html

6.30 KB

03. Solution Convolutional Layers.html

6.74 KB

05. Quiz Max Pooling Layers.html

7.42 KB

04. Max Pooling Layers.html

7.80 KB

02. Quiz Convolutional Layers.html

8.75 KB

01. Convolutional Layers.html

9.48 KB

07. CNNs in TensorFlow.html

12.71 KB

img

max-pooling.png

25.19 KB

convolution-schematic.gif

63.63 KB

arch.png

1.20 MB

img

part-header-2.jpg

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index.html

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