Release v0.16.0

Brand-New Attention Chapter

We have added the brand-new Chapter: Attention Mechanisms:

• Attention Cues

  • Attention Cues in Biology
  • Queries, Keys, and Values
  • Visualization of Attention

• Attention Pooling: Nadaraya-Watson Kernel Regression

  • Generating the Dataset
  • Average Pooling
  • Nonparametric Attention Pooling
  • Parametric Attention Pooling

• Attention Scoring Functions

  • Masked Softmax Operation
  • Additive Attention
  • Scaled Dot-Product Attention

• Bahdanau Attention

  • Model
  • Defining the Decoder with Attention
  • Training

• Multi-Head Attention

  • Model
  • Implementation

• Self-Attention and Positional Encoding

  • Self-Attention
  • Comparing CNNs, RNNs, and Self-Attention
  • Positional Encoding

• Transformer

  • Model
  • Positionwise Feed-Forward Networks
  • Residual Connection and Layer Normalization
  • Encoder
  • Decoder
  • Training

PyTorch Adaptation Completed

We have completed PyTorch implementations for Vol.1 (Chapter 1--15).

Towards v1.0

The following chapters have been significantly improved for v1.0:

• Introduction
• Modern Recurrent Neural Networks

Chinese Translation

The following chapters have been translated into Chinese (d2l-zh v2 Git repo, Web preview):

• Introduction
• Preliminaries
• Linear Neural Networks
• Multilayer Perceptrons
• Deep Learning Computation
• Convolutional Neural Networks
• Modern Convolutional Neural Networks

Turkish Translation

The community are translating the book into Turkish (d2l-tr Git repo, Web preview). The first draft of Chapter 1--7 is complete.