This release is a book publication announcement, not a software release. No code changes, dependencies, or developer action required.

D2L has gone 1.0.0-beta0! We thank all the 296 contributors for making this happen!

Forthcoming on Cambridge University Press

Chapter 1--11 will be forthcoming on Cambridge University Press (early 2023).

New JAX...

Add PaddlePaddle implementation for the d2l library (compatible with classic.d2l.ai). See the PR in d2l-zh.

We are happy to release D2L 1.0.0-alpha1.post0 We thank all the contributors who have made this open-source textbook better for everyone.

This minor release includes the following updates:

• Build PDFs using Cambridge Latex Style (#2187)
• Feat: Add dynamic preview/stable version tab (#2264)
• Chapter Modern RNN is heavily improv...

We are excited to announce the release of D2L 1.0.0-alpha0! We thank all the 265 contributors who have made this open-source textbook better for everyone.

New Topics and Revision

We have added the following new topics, with discussions of more recent methods such as ResNeXt, RegNet, ConvNeXt, Vision Transformer, Swin Transformer,...

This release fixes issues when installing d2l package and running d2l notebooks on Google Colab with Python 3.7 and updates PyTorch & TensorFlow to their respective latest versions.

More concretely, this release includes the following upgrades/fixes:

• Update TensorFlow==2.8.0 (#2055)
• Update PyTorch: torch==1.11.0 & torchvision==0.12.0 (#2063)
• Rollback NumPy==1.21.5 & Support Python>=...

This release supports running the book with SageMaker Studio Lab for free and introduces several fixes:

• Fix data synchronization for multi-GPU training in PyTorch (https://github.com/d2l-ai/d2l-en/pull/1978)
• Fix token sampling in BERT datasets (https://github.com/d2l-...

Dive into Deep Learning is now available on arxiv!

Framework Adaptation

We have added TensorFlow implementations up to Chapter 11 (Optimization Algorithms).

Towards v1.0

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

• Optimization (the first 4 sections)
• Computational Performance
• Computer Vision
• Natural Language...

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 S...

Framework Adaptation

We have added PyTorch implementations up to Chapter 11 (Optimization Algorithms). Chapter 1--7 and Chapter 11 have also been adapted to TensorFlow.

Towards v1.0

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

• Linear Neural Networks
• Multilayer Perceptrons
• Deep Learning Computation
• Convolutional Neural Networks
• Modern Convolutiona...