d2l-pytorch

Ch06 Multilayer Perceptrons

• 6.1 Multilayer Perceptron
• 6.2 Implementation of Multilayer Perceptron from Scratch
• 6.3 Concise Implementation of Multilayer Perceptron
• 6.4 Model Selection Underfitting and Overfitting
• 6.5 Weight Decay
• 6.6 Dropout
• 6.7 Forward Propagation Backward Propagation and Computational Graphs
• 6.8 Numerical Stability and Initialization
• 6.9 Considering the Environment
• 6.10 Predicting House Prices on Kaggle

Ch07 Deep Learning Computation

• 7.1 Layers and Blocks
• 7.2 Parameter Management
• 7.3 Deferred Initialization
• 7.4 Custom Layers
• 7.5 File I/O
• 7.6 GPUs

Ch08 Convolutional Neural Networks

• 8.1 From Dense Layers to Convolutions
• 8.2 Convolutions for Images
• 8.3 Padding and Stride
• 8.4 Multiple Input and Output Channels
• 8.5 Pooling
• 8.6 Convolutional Neural Networks (LeNet)

Ch09 Modern Convolutional Networks

• 9.1 Deep Convolutional Neural Networks (AlexNet)
• 9.2 Networks Using Blocks (VGG)
• 9.3 Network in Network (NiN)
• 9.4 Networks with Parallel Concatenations (GoogLeNet)
• 9.5 Batch Normalization
• 9.6 Residual Networks (ResNet)
• 9.7 Densely Connected Networks (DenseNet)

Ch10 Recurrent Neural Networks

• 10.1 Sequence Models
• 10.2 Language Models
• 10.3 Recurrent Neural Networks
• 10.4 Text Preprocessing
• 10.5 Implementation of Recurrent Neural Networks from Scratch
• 10.6 Concise Implementation of Recurrent Neural Networks
• 10.7 Backpropagation Through Time
• 10.8 Gated Recurrent Units (GRU)
• 10.9 Long Short Term Memory (LSTM)
• 10.10 Deep Recurrent Neural Networks
• 10.11 Bidirectional Recurrent Neural Networks
• 10.12 Machine Translation and DataSets
• 10.13 Encoder-Decoder Architecture
• 10.14 Sequence to Sequence
• 10.15 Beam Search

Ch11 Attention Mechanism

• 11.1 Attention Mechanism
• 11.2 Sequence to Sequence with Attention Mechanism
• 11.3 Transformer

Ch12 Optimization Algorithms

• 12.1 Optimization and Deep Learning
• 12.2 Convexity
• 12.3 Gradient Descent
• 12.4 Stochastic Gradient Descent
• 12.5 Mini-batch Stochastic Gradient Descent
• 12.6 Momentum
• 12.7 Adagrad
• 12.8 RMSProp
• 12.9 Adadelta
• 12.10 Adam

Ch14 Computer Vision

• 14.1 Image Augmentation
• 14.2 Fine Tuning
• 14.3 Object Detection and Bounding Boxes
• 14.4 [Anchor Boxes](https:/