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[CourseClub NET] Coursera - Introduction to Deep Learning
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Torrent Files List
001.Specialization Promo (Size: 1.27 GB) (Files: 81)
001.Specialization Promo
001. Welcome to AML specialization!.mp4
001. Welcome to AML specialization!.srt
002.Course intro
002. Course intro.mp4
002. Course intro.srt
003.Linear model as the simplest neural network
003. Linear regression.mp4
003. Linear regression.srt
004. Linear classification.mp4
004. Linear classification.srt
005. Gradient descent.mp4
005. Gradient descent.srt
004.Regularization in machine learning
006. Overfitting problem and model validation.mp4
006. Overfitting problem and model validation.srt
007. Model regularization.mp4
007. Model regularization.srt
005.Stochastic methods for optimization
008. Stochastic gradient descent.mp4
008. Stochastic gradient descent.srt
009. Gradient descent extensions.mp4
009. Gradient descent extensions.srt
006.The simplest neural network MLP
010. Multilayer perceptron (MLP).mp4
010. Multilayer perceptron (MLP).srt
011. Chain rule.mp4
011. Chain rule.srt
012. Backpropagation.mp4
012. Backpropagation.srt
007.Matrix derivatives
013. Efficient MLP implementation.mp4
013. Efficient MLP implementation.srt
014. Other matrix derivatives.mp4
014. Other matrix derivatives.srt
008.TensorFlow framework
015. What is TensorFlow.mp4
015. What is TensorFlow.srt
016. Our first model in TensorFlow.mp4
016. Our first model in TensorFlow.srt
009.Philosophy of deep learning
017. What Deep Learning is and is not.mp4
017. What Deep Learning is and is not.srt
018. Deep learning as a language.mp4
018. Deep learning as a language.srt
010.Introduction to CNN
019. Motivation for convolutional layers.mp4
019. Motivation for convolutional layers.srt
020. Our first CNN architecture.mp4
020. Our first CNN architecture.srt
011.Modern CNNs
021. Training tips and tricks for deep CNNs.mp4
021. Training tips and tricks for deep CNNs.srt
022. Overview of modern CNN architectures.mp4
022. Overview of modern CNN architectures.srt
012.Applications of CNNs
023. Learning new tasks with pre-trained CNNs.mp4
023. Learning new tasks with pre-trained CNNs.srt
024. A glimpse of other Computer Vision tasks.mp4
024. A glimpse of other Computer Vision tasks.srt
013.Intro to Unsupervised Learning
025. Unsupervised learning what it is and why bother.mp4
025. Unsupervised learning what it is and why bother.srt
026. Autoencoders 101.mp4
026. Autoencoders 101.srt
014.More Autoencoders
027. Autoencoder applications.mp4
027. Autoencoder applications.srt
028. Autoencoder applications image generation, data visualization & more.mp4
028. Autoencoder applications image generation, data visualization & more.srt
015.Word Embeddings
029. Natural language processing primer.mp4
029. Natural language processing primer.srt
030. Word embeddings.mp4
030. Word embeddings.srt
016.Generative Adversarial Networks
031. Generative models 101.mp4
031. Generative models 101.srt
032. Generative Adversarial Networks.mp4
032. Generative Adversarial Networks.srt
033. Applications of adversarial approach.mp4
033. Applications of adversarial approach.srt
017.Introduction to RNN
034. Motivation for recurrent layers.mp4
034. Motivation for recurrent layers.srt
035. Simple RNN and Backpropagation.mp4
035. Simple RNN and Backpropagation.srt
018.Modern RNNs
036. The training of RNNs is not that easy.mp4
036. The training of RNNs is not that easy.srt
037. Dealing with vanishing and exploding gradients.mp4
037. Dealing with vanishing and exploding gradients.srt
038. Modern RNNs LSTM and GRU.mp4
038. Modern RNNs LSTM and GRU.srt
019.Applications of RNNs
039. Practical use cases for RNNs.mp4
039. Practical use cases for RNNs.srt
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