Deep Learning

An MIT Press book in preparation

Ian Goodfellow, Yoshua Bengio and Aaron Courville

Book   Exercises External Links  


We plan to offer lecture slides accompanying all chapters of this book. We currently offer slides for only some chapters. If you are a course instructor and have your own lecture slides that are relevant, feel free to contact us if you would like to have your slides linked or mirrored from this site.

  1. Introduction
  2. Linear Algebra [.key][.pdf]
  3. Probability and Information Theory [.key][.pdf]
  4. Numerical Computation [.key] [.pdf] [youtube]
  5. Machine Learning Basics [.key] [.pdf]
  6. Deep Feedforward Networks [.key] [.pdf]
  7. Regularization for Deep Learning [.pdf] [.key]
  8. Optimization for Training Deep Models
  9. Convolutional Networks
  10. Sequence Modeling: Recurrent and Recursive Networks
  11. Practical Methodology [.key][.pdf] [youtube]
  12. Applications [.key][.pdf]
  13. Linear Factors [.key][.pdf]
  14. Autoencoders [.key][.pdf]
  15. Representation Learning [.key][.pdf]
  16. Structured Probabilistic Models for Deep Learning [.key][.pdf]
  17. Monte Carlo Methods [.key] [.pdf]
  18. Confronting the Partition Function [.key] [.pdf]