Artificial Intelligence Foundations Neural Networks

  0 - Introduction

   1. Neural networks 101 Your path to AI brilliance.srt

   2. What you should know.mp4

   1. Neural networks 101 Your path to AI brilliance.mp4

  5 - Best Practices for Optimizing a Neural Network

   6. Solution Manually tune hyperparameters.srt

   3. How do you improve model performance.srt

   4. Regularization techniques to improve overfitting models.srt

   2. Hyperparameters and neural networks.mp4

   3. How do you improve model performance.mp4

   4. Regularization techniques to improve overfitting models.mp4

  4 - Build a Simple Neural Network Using Keras

   4. Data preprocessing.srt

   7. Solution Build a neural network.srt

   2. Use case and determine evaluation metric.srt

   6. Challenge Build a neural network.mp4

   3. Data checks and data preparation.mp4

   2. Use case and determine evaluation metric.mp4

   7. Solution Build a neural network.mp4

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  1 - What Are Neural Networks

   2. Biological neural networks.srt

   1. Machine learning and neural networks.srt

   2. Biological neural networks.mp4

   1. Machine learning and neural networks.mp4

  6 - Conclusion

   1. Next steps.mp4

  2 - Key Components in Neural Network Architecture

   3. Transfer and activation functions.srt

   4. How neural networks learn.srt

   3. Transfer and activation functions.mp4

   4. How neural networks learn.mp4

  3 - Other Types of Neural Networks

   2. Recurrent neural networks (RNN).srt

   3. Transformer architecture.mp4

   1. Convolutional neural networks (CNN).mp4

 Reinforcement Learning Foundations

  2 - Reinforcement Learning Algorithms

   2. Temporal difference methods.srt

   2. Temporal difference methods.mp4

   1. Monte Carlo method.mp4

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  3 - Monte Carlo Method

   1. The setting.srt

   4. First visit and every visit MC prediction.srt

   6. Additional modifications.srt

   3. Monte Carlo prediction.mp4

   6. Additional modifications.mp4

   2. Exploration and exploitation.mp4

  0 - Introduction

   1. Reinforcement learning in a nutshell.mp4

  5 - Modified Forms of Reinforcement

   3. Inverse reinforcement learning.srt

   2. Multi-agent reinforcement learning.mp4

   1. Deep reinforcement learning.mp4

  4 - Temporal Difference Methods

   4. Expected SARSA.srt

   2. SARSA.srt

   4. Expected SARSA.mp4

   2. SARSA.mp4

  6 - Conclusion

   1. Your reinforcement learning journey.mp4

  1 - Getting Started with Reinforcement Learning

   1. Terms in reinforcement learning.srt

   3. Markov decision process.srt

   1. Terms in reinforcement learning.mp4

   3. Markov decision process.mp4

 Deep Learning Getting Started

  7 - Conclusion

   1. Extending your deep learning education.mp4

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  6 - Deep Learning Exercise

   4. Predicting root causes with deep learning.srt

   1. Exercise problem statement.srt

   3. Building the RCA model.mp4

   1. Exercise problem statement.mp4

  0 - Introduction

   3. Setting up the environment.srt

   1. Getting started with deep learning.mp4

   3. Setting up the environment.mp4

  4 - Deep Learning Example 1

   1. The Iris classification problem.srt

   3. Creating a deep learning model.srt

   4. Training and evaluation.srt

   6. Predictions with deep learning models.mp4

   3. Creating a deep learning model.mp4

   4. Training and evaluation.mp4

  5 - Deep Learning Example 2

   4. Predictions for text.srt

   2. Creating text representations.srt

   4. Predictions for text.mp4

   2. Creating text representations.mp4

  3 - Training a Neural Network

   5. Gradient descent.srt

   8. An ANN model.srt

   3. Measuring accuracy and error.srt

   6. Batches and epochs.srt

   1. Setup and initialization.srt

   5. Gradient descent.mp4

   8. An ANN model.mp4

   10. Using available open-source models.mp4

   4. Back propagation.mp4

   1. Setup and initialization.mp4

  1 - Introduction to Deep Learning

   1. What is deep learning.srt

   2. Linear regression.srt

   3. An analogy for deep learning.srt

   1. What is deep learning.mp4

   6. Training an ANN.mp4

   5. Artificial neural networks.mp4

  2 - Neural Network Architecture

   2. Hidden layers.srt

   3. Weights and biases.srt

   5. The output layer.mp4

   2. Hidden layers.mp4

   1. The input layer.mp4

  Ex_Files_Deep_Learning_Getting_Started.zip

 Hands-On PyTorch Machine Learning

  0 - Introduction

   1. Explore the capabilities of PyTorch.mp4

  6 - Conclusion

   1. Continuing your PyTorch learning process.mp4

  3 - Torchvision

   1. Torchvision introduction.srt

   1. Torchvision introduction.mp4

  2 - PyTorch Basics

   2. Understand PyTorch basic operations.srt

   3. Understand PyTorch NumPy Bridge.srt

   5. Advanced PyTorch autograd.mp4

   1. Understand PyTorch tensors.mp4

   3. Understand PyTorch NumPy Bridge.mp4

  1 - Preparation

   4. PyTorch data exploration.srt

   1. PyTorch overview.srt

   1. PyTorch overview.mp4

   2. PyTorch environment setup.mp4

  4 - Torchaudio

   2. Torchaudio for audio understanding.srt

   2. Torchaudio for audio understanding.mp4

  5 - Torchtext

   2. Torchtext for translation.srt

   2. Torchtext for translation.mp4

 Machine Learning Foundations Linear Algebra

  8 - Conclusion

   1. Next steps.mp4

  0 - Introduction

   2. What you should know.srt

   1. Introduction.mp4

  6 - Matrices from Orthogonality to Gram–Schmidt Process

   1. Matrices changing basis.srt

   4. Gram–Schmidt process.srt

   1. Matrices changing basis.mp4

   2. Transforming to the new basis.mp4

  1 - Introduction to Linear Algebra

   2. Applications of linear algebra in ML.srt

   2. Applications of linear algebra in ML.mp4

  4 - Introduction to Matrices

   2. Types of matrices.srt

   3. Types of matrix transformation.srt

   3. Types of matrix transformation.mp4

   4. Composition or combination of matrix transformations.mp4

  5 - Gaussian Elimination

   2. Gaussian elimination and finding the inverse matrix.srt

   3. Inverse and determinant.mp4

   1. Solving linear equations using Gaussian elimination.mp4

  7 - Eigenvalues and Eigenvectors

   2. Calculating eigenvalues and eigenvectors.srt

   4. Google PageRank algorithm.srt

   2. Calculating eigenvalues and eigenvectors.mp4

   3. Changing to the eigenbasis.mp4

  3 - Vector Projections and Basis

   1. Dot product of vectors.srt

   3. Changing basis of vectors.srt

   1. Dot product of vectors.mp4

   3. Changing basis of vectors.mp4

  2 - Vectors Basics

   2. Vector arithmetic.srt

   3. Coordinate system.mp4

   1. Introduction to vectors.mp4

 Building Computer Vision Applications with Python

  8 - Conclusion

   1. Next steps.mp4

  5 - Image Scaling

   6. Solution Resize a picture.srt

   1. Image downscaling methods.srt

   4. Upscaling example.srt

   3. Image upscaling methods.mp4

   6. Solution Resize a picture.mp4

   4. Upscaling example.mp4

  3 - From Color to Black and White

   6. Solution Removing color.srt

   3. Converting grayscale to black and white.srt

   4. Adaptive thresholding.srt

   6. Solution Removing color.mp4

   3. Converting grayscale to black and white.mp4

   4. Adaptive thresholding.mp4

  0 - Introduction

   1. Computer vision under the hood.srt

   3. Using the exercise files.mp4

   1. Computer vision under the hood.mp4

  1 - Setting Up Your Environment

   2. Testing your environment.srt

   2. Testing your environment.mp4

  4 - Filters

   6. Challenge Convolution filters.srt

   2. Average filters.srt

   1. Convolution filters.srt

   6. Challenge Convolution filters.mp4

   4. Gaussian filters.mp4

   2. Average filters.mp4

   3. Median filters.mp4

  6 - Fun with Cuts

   5. Solution Stitch two pictures together.srt

   1. Image cuts.srt

   4. Challenge Stitch two pictures together.mp4

   3. Cuts in panoramic photography.mp4

   2. Stitching two images together.mp4

  7 - Morphological Modifications

   3. Open and close.srt

   2. Erosion and dilation.srt

   3. Open and close.mp4

   4. Challenge Help a robot.mp4

   1. Why modify objects.mp4

  2 - The Basics of Image Processing

   6. Challenge Manipulate some pictures.srt

   2. Color encoding.srt

   1. Image representation.srt

   5. Rotations and flips.mp4

   2. Color encoding.mp4

   7. Solution Manipulate some pictures.mp4

   3. Image file management.mp4

 Artificial Intelligence Foundations Thinking Machines

  8 - Conclusion

   1. Next steps.mp4

  0 - Introduction

   1. Welcome.mp4

  6 - What Has Changed

   2. Foundation models.srt

   3. Self-supervised learning.mp4

   2. Foundation models.mp4

  4 - Common AI Programs

   1. Robotics.srt

   3. The Internet of Things.mp4

   2. Natural language processing.mp4

  3 - Finding the Right Approach

   2. Data vs. reasoning.srt

   1. Match patterns.srt

   2. Data vs. reasoning.mp4

   5. Regression.mp4

   1. Match patterns.mp4

  5 - Mixing with Other Technologies

   2. Data science.srt

   2. Data science.mp4

  2 - The Rise of Machine Learning

   1. Machine learning.srt

   2. Artificial neural networks.mp4

   3. Perceptrons.mp4

  1 - What Is Artificial Intelligence

   3. Strong vs. weak AI.srt

   1. Define general intelligence.srt

   1. Define general intelligence.mp4

   4. Plan AI.mp4

  7 - Avoiding Pitfalls

   1. Pitfalls.mp4