Deep Learning is available chapter by chapter for free on the book [website](https://www.deeplearningbook.org/). > [!Example]+ Table of Contents> > - [Table of Contents](https://www.deeplearningbook.org/contents/TOC.html) > - [Acknowledgements](https://www.deeplearningbook.org/contents/acknowledgements.html) > - [Notation](https://www.deeplearningbook.org/contents/notation.html) > - [Introduction](https://www.deeplearningbook.org/contents/intro.html) > - [Part I: Applied Math and Machine Learning Basics](https://www.deeplearningbook.org/contents/part_basics.html) > - [2 Linear Algebra](https://www.deeplearningbook.org/contents/linear_algebra.html) > - [3 Probability and Information Theory](https://www.deeplearningbook.org/contents/prob.html) > - [4 Numerical Computation](https://www.deeplearningbook.org/contents/numerical.html) > - [5 Machine Learning Basics](https://www.deeplearningbook.org/contents/ml.html) > - [Part II: Modern Practical Deep Networks](https://www.deeplearningbook.org/contents/part_practical.html) > - [6 Deep Feedforward Networks](https://www.deeplearningbook.org/contents/mlp.html) > - [7 Regularization for Deep Learning](https://www.deeplearningbook.org/contents/regularization.html) > - [8 Optimization for Training Deep Models](https://www.deeplearningbook.org/contents/optimization.html) > - [9 Convolutional Networks](https://www.deeplearningbook.org/contents/convnets.html) > - [10 Sequence Modeling: Recurrent and Recursive Nets](https://www.deeplearningbook.org/contents/rnn.html) > - [11 Practical Methodology](https://www.deeplearningbook.org/contents/guidelines.html) > - [12 Applications](https://www.deeplearningbook.org/contents/applications.html) > - [Part III: Deep Learning Research](https://www.deeplearningbook.org/contents/part_research.html) > - [13 Linear Factor Models](https://www.deeplearningbook.org/contents/linear_factors.html) > - [14 Autoencoders](https://www.deeplearningbook.org/contents/autoencoders.html) > - [15 Representation Learning](https://www.deeplearningbook.org/contents/representation.html) > - [16 Structured Probabilistic Models for Deep Learning](https://www.deeplearningbook.org/contents/graphical_models.html) > - [17 Monte Carlo Methods](https://www.deeplearningbook.org/contents/monte_carlo.html) > - [18 Confronting the Partition Function](https://www.deeplearningbook.org/contents/partition.html) > - [19 Approximate Inference](https://www.deeplearningbook.org/contents/inference.html) > - [20 Deep Generative Models](https://www.deeplearningbook.org/contents/generative_models.html) > - [Bibliography](https://www.deeplearningbook.org/contents/bib.html) > - [Index](https://www.deeplearningbook.org/contents/index-.html)