MIT.Press.Deep.Learning.2016

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Table of Contents Chapter 1 Introduction Part I: Applied Math and Machine Learning Basics Chapter 2 Linear Algebra Chapter 3 Probability and Information Theory Chapter 4 Numerical Computation Chapter 5 Machine Learning Basics Part II: Modern Practical Deep Networks Chapter 6 Deep Feedforward Networks Chapter 7 Regularization Chapter 8 Optimization for Training Dee p Models Chapter 9 Convolutional Networks Chapter 10 Sequence Modeling: Recurrent and Recursive Nets Chapter 11 Practical Methodology Chapter 12 Applications Part III: Deep Learning Research Chapter 13 Linear Factor Models Chapter 14 Autoencoders Chapter 15 Representation Learning Chapter 16 Structured Probabilistic Models for Deep Learning Chapter 17 Monte Carlo Methods Chapter 18 Confronting the Partition Function Chapter 19 Approximate Inference Chapter 20 Deep Generative Models p Models Chapter 9 Convolutional Networks Chapter 10 Sequence Modeling: Recurrent and Recursive Nets Chapter 11 Practical Methodology Chapter 12 Applications Part III: Deep Learning Research Chapter 13 Linear Factor Models Chapter 14 Autoencoders Chapter 15 Representation Learning Chapter 16 Structured Probabilistic Models for Deep Learning Chapter 17 Monte Carlo Methods Chapter 18 Confronting the Partition Function Chapter 19 Approximate Inference Chapter 20 Deep Generative Models