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@astonzhang astonzhang released this Dec 18, 2019 · 174 commits to master since this release

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.

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