Goodfellow I., Bengio Y., Courville A. Deep Learning

Online version, The MIT Press, 2016. 716 p.This book has been organized into three parts to best accommodate a variety of readers. Part I introduces basic mathematical tools and machine learning concepts. Part II describes the most established deep learning algorithms, which are essentially solved technologies. Part III describes more speculative ideas that are widely believed to be important for future research in deep learning.Deep learning has already proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising, and finance. This book can be useful for a variety of readers, although it was written with two target audiences in mind: university students (undergraduate or graduate) learning about machine learning, and software engineers who do not have a machine learning or statistics background but want to rapidly acquire one and begin using deep learning in their product or platform.