Highlights

• D2L is now based on the NumPy interface. All the code samples are rewritten.

New Contents

• Recommender Systems

  • Overview of Recommender Systems
  • The MovieLens Dataset
  • Matrix Factorization
  • AutoRec: Rating Prediction with Autoencoders
  • Personalized Ranking for Recommender Systems
  • Neural Collaborative Filtering for Personalized Ranking
  • Sequence-Aware Recommender Systems
  • Feature-Rich Recommender Systems
  • Factorization Machines
  • Deep Factorization Machines

• Appendix: Mathematics for Deep Learning

  • Geometry and Linear Algebraic Operations
  • Eigendecompositions
  • Single Variable Calculus
  • Multivariable Calculus
  • Integral Calculus
  • Random Variables
  • Maximum Likelihood
  • Distributions
  • Naive Bayes
  • Statistics
  • Information Theory

• Attention Mechanisms

  • Attention Mechanism
  • Sequence to Sequence with Attention Mechanism
  • Transformer

• Generative Adversarial Networks

  • Generative Adversarial Networks
  • Deep Convolutional Generative Adversarial Networks

• Preliminaries

  • Data Preprocessing
  • Calculus

Improvements

• The Preliminaries chapter is improved.
• More theoretical analysis is added to the Optimization chapter.

Preview Version

Hard copies of a D2L preview version based on this release (excluding chapters of Recommender Systems and Generative Adversarial Networks) are distributed at AWS re:Invent 2019 and NeurIPS 2019.