1 Introduction
2 Deep Learning
2.1 Linear Classification
2.2 Deep Learning
2.3 Discriminative and Generative Modeling
2.4 The Variational Autoencoder (VAE) framework
2.5 The Generative Adversarial Network (GAN) Framework
3 Tensors
3.1 Mapping High-Dimensional Arrays to Memory
3.2 Tensor, TensorImpl, and StorageImpl
3.3 Torch Dispatching and Operators
3.4 Torch Gen
4 Automatic Differentiation and Computation Graph
4.1 Building Computation Graphs
4.2 Deriving the Computation Graph by Hand
4.3 Executing The Computation Graph
5 Torch Layers
5.1 Linear
5.2 Sigmoid
5.3 Dropout
5.4 Batch Normalization (BN)
5.5 Convolutional Neural Networks (CNNs)
5.6 MSE Loss
6 Optimization
6.1 Stochastic Gradient Descent
6.2 Optimizer Step
7 Adversarial Attacks
8 Inference Engines
A Monte Carlo
B Information Theory
C Intrusive Pointers