- D2L is now based on the NumPy interface. All the code samples are rewritten.
- 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
- Single Variable Calculus
- Multivariable Calculus
- Integral Calculus
- Random Variables
- Maximum Likelihood
- Naive Bayes
- Information Theory
- Attention Mechanism
- Sequence to Sequence with Attention Mechanism
Generative Adversarial Networks
- Generative Adversarial Networks
- Deep Convolutional Generative Adversarial Networks
- Data Preprocessing
- The Preliminaries chapter is improved.
- More theoretical analysis is added to the Optimization chapter.
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.