Inside Deep Learning: Math, Algorithms, Models MEAP V01
Copyright
welcome
brief contents
1: The Mechanics of Learning
1.1 Getting Started with Colab
1.2 The World as Tensors
1.2.1 PyTorch GPU Acceleration
1.3 Automatic Differentiation
1.4 Optimizing Parameters
1.5 Loading DataSet Objects
1.5.1 Creating a Training and Testing Split
1.6 Exercises
1.7 Summary
2: Fully Connected Networks
2.1 Neural Networks as Optimization
2.1.1 Linear Regression
2.1.2 The Training Loop
2.1.3 Defining the Model
2.1.4 Defining the Loss Function
2.2 Building Our First Neural Network
2.2.1 Adding Non-Linearities
2.3 Classification Problems
2.3.1 Classification Toy Problem
2.3.2 Classification Loss Function
2.4 Better Training Code
2.4.1 Custom Metrics
2.4.2 Training and Testing Passes
2.4.3 Saving Checkpoints
2.4.4 Putting It All Together
2.5 Training in Batches
2.6 Exercises
2.7 Chapter summary
3: Convolutional Neural Networks
3.1 Spatial Structural Prior Beliefs
3.1.1 Loading MNIST with PyTorch
3.2 What are Convolutions?
3.2.1 1D Convolutions
3.2.2 2D Convolutions
3.2.3 Padding
3.2.4 Weight Sharing
3.3 Utility of Convolutions
3.4 Putting it to Practice, our first CNN
3.5 Pooling And Object Location
3.5.1 CNNs with Max Pooling
3.6 Data Augmentation
3.7 Exercises
3.8 Chapter summary