In-depth learning

by Ian Goodfellow

In-depth learningb A complete reference book and bible for in-depth study! b A book that introduces various topics of in-depth study. This course introduces several key concepts of linear algebra, probabilistic theory, information theory, numerical computation, and machine learning related to in-depth learning, and then introduces several concepts used by industry practitioners such as in-depth forward neural networks, regularization, optimization algorithms, It explains in-depth learning techniques and introduces realistic in-depth learning practice methodology. It also outlines methods for applying in-depth learning for natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, we examine in-depth learning from the point of view of the research, such as the theory of linear factors, automatic encoders, expressive learning, structural probability models, and Monte Carlo methods.

Paperback

Published October 31, 2018

About the author

Ian J. Goodfellow is a researcher working in machine learning, currently employed at Apple Inc. as its director of machine learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain.