Deep Learning from Scratch. Building with Python from First Principles Seth Weidman

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Spis treści książki

• Preface
  • Understanding neural networks requires multiple mental models
  • Chapter outlines
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Foundations
  • Functions
    • Math
    • Diagram
    • Code
      • Code caveat #1: Numpy
      • Code caveat #2: Type checked functions
      • Basic functions in Numpy
  • Derivatives
    • Math
    • Diagram(s)
    • Code
  • Nested Functions
    • Diagram
    • Math
    • Code
    • Another diagram
  • The Chain Rule
    • Math
      • Diagram
    • Code
  • A slightly longer example
    • Math
    • Diagram
    • Code
  • Functions With Multiple Inputs
    • Math
    • Diagram
    • Code
  • Derivatives of Functions with Multiple Inputs
    • Diagram
    • Math
    • Code
  • Functions with multiple vector inputs
    • Math
  • Creating new features from existing features
    • Math
    • Diagram
    • Code
  • Derivatives of functions with multiple vector inputs
    • Diagram
    • Math
    • Code
  • Vector functions and their derivatives - one step further
    • Diagram
    • Math
    • Code
    • Vector functions and their derivatives: the backward pass
      • Math
      • Diagram
      • Code
      • Is this right?
  • Computational Graph with Two 2D Matrix Inputs
    • Math
    • Diagram
    • Code
  • The fun part: the backward pass
    • Diagram
    • Math
      • The ?
      • The Answer
    • Code
      • Describing these gradients visually
  • Conclusion
• 2. Fundamentals
  • Supervised Learning Overview
  • Supervised Learning models
  • Linear regression
    • Linear regression: a diagram
      • Training this model
    • Linear regression: a more helpful diagram (and the math)
    • Adding in the intercept
    • Linear regression: the code
  • Training the model
    • Calculating the gradients: a diagram
    • Calculating the gradients: the math (and some code)
    • Calculating the gradients: the (full) code
    • Using these gradients to train the model
  • Assessing our model: training set vs. testing set
  • Assessing our model: the code
    • Analyzing the most important feature
  • Neural networks from scratch
    • Step 1: a bunch of linear regressions
    • Step 2: a non-linear function
    • Step 3: another linear regression
    • Diagram(s)
      • Another diagram?
    • Code
    • Neural networks: the backward pass
      • Diagram
      • Math (and code)
      • The overall loss gradient
  • Training and assessing our first neural network
    • Two reasons why this is happening
  • Conclusion
• 3. Deep Learning From Scratch
  • Deep Learning definition: a first pass
  • The building blocks of neural networks: Operations
    • Diagram
    • Code
  • The building blocks of neural networks: Layers
    • Diagrams
      • Connection to the brain
  • Building blocks on building blocks
    • The Layer blueprint
    • The Dense Layer
  • The NeuralNetwork class, and maybe others
    • Diagram
    • Code
    • Loss class
  • Deep Learning From Scratch
    • Implementing batch training
    • NeuralNetwork: code
  • Trainer and Optimizer
    • Optimizer
      • Description and code
    • Trainer
      • Trainer code
  • Putting everything together
    • Our first Deep Learning model (from scratch)
  • Conclusion and next steps
• 4. Extensions
  • Some Intuition about Neural Networks
  • The softmax cross entropy loss function
    • Component #1: the softmax function
      • Math
      • Intuition
    • Component #2: the cross entropy loss
      • Math
      • Intuition
      • Code
    • A note on activation functions
      • The other extreme: the Rectified Linear Unit
      • A happy medium: Tanh
  • Experiments
    • Data preprocessing
    • Model
    • Experiment: softmax cross entropy loss
  • Momentum
    • Intuition for momentum
    • Implementing momentum in the Optimizer class
      • Math
      • Code
    • Experiment: stochastic gradient descent with momentum
  • Learning rate decay
    • Types of learning rate decay
    • Experiments: learning rate decay
  • Weight initialization
    • Math and code
    • Experiments: weight initialization
  • Dropout
    • Dropout: definition
    • Dropout: implementation
      • Adjusting the rest of our framework to accommodate Dropout
    • Experiments: dropout
  • Conclusion
• 5. Convolutional Neural Networks
  • Neural networks and representation learning
    • A different architecture for image data
    • The convolution operation
    • The multi-channel convolution operation
  • Convolutional Layers
    • Implementation implications
    • The differences between convolutional and fully connected layers
    • Making predictions with convolutional layers: the Flatten layer
    • Pooling layers
      • Applying CNNs beyond images
  • Implementing the multi-channel convolution operation
    • The forward pass
      • Diagrams and math
      • Padding
      • Code
      • A note on stride
    • Convolutions: the backward pass
      • What should the gradient be?
      • Computing the gradient of a 1D convolution
      • Whats the general pattern?
      • Computing the parameter gradient
      • Coding this up
    • Batches, 2D Convolutions, and Multiple Channels
      • 1D convolutions with batches: forward pass
      • 1D convolution with batches: backward pass
    • 2D convolutions
      • 2D convolutions: coding the forward pass
      • 2D convolutions: coding the backward pass
    • The last element: adding channels
      • Forward pass
      • Backward pass
  • Using this Operation to Train a CNN
    • The Flatten Operation
    • The full Conv2D Layer
      • A note on speed, and an alternative implementation
    • Experiments
  • Conclusion
• 6. Recurrent Neural Networks
  • The Key Limitation: handling branching
  • Automatic differentiation
    • Coding up gradient accumulation
      • Automatic differentiation illustration
      • Explaining what happened
  • Motivation for recurrent neural networks
  • Introduction to Recurrent Neural Networks
    • The first class for RNNs: RNNLayer
    • The second class for RNNs: RNNNode
    • Putting these two classes together
    • The backward pass
      • Accumulating gradients for the weights in an RNNs
  • RNNs: the code
    • The RNNLayer class
      • Initialization
      • The forward method
      • The backward method
    • The essential elements of RNNNodes
    • Vanilla RNNNodes
      • RNNNode: the code
      • RNNNodes: the backward pass
    • Limitations of vanilla RNNNodes
    • One solution: GRUNodes
      • GRUNodes: a diagram
      • GRUNodes: the code
    • LSTMNodes
      • LSTMNode: diagram
      • LSTMs: the code
    • Data representation for a character-level RNN-based language model
    • Other language modeling tasks
    • Combining RNNLayer variants
    • Putting this all together
  • Conclusion
• 7. PyTorch
  • PyTorch Tensors
  • Deep Learning with PyTorch
    • PyTorch elements: Model, Layer Optimizer, and Loss
      • The inference flag
    • Implementing neural network building blocks using PyTorch: DenseLayer
    • Example: Boston Housing Prices Model in PyTorch
    • PyTorch elements: Optimizer and Loss
    • PyTorch elements: Trainer
    • Tricks to optimize learning in PyTorch
  • Convolutional neural networks in PyTorch
    • DataLoader and transforms
    • LSTMs in PyTorch
  • Postscript: Unsupervised Learning via Autoencoders
    • Representation Learning
    • An approach for situations with no labels whatsoever
      • Diagram
    • Implementing an autoencoder in PyTorch
    • A stronger test for unsupervised learning, and a solution
  • Conclusion
• A. Appendix
  • Matrix chain rule
  • Gradient of the loss with respect to the bias terms
  • Convolutions via matrix multiplication

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