Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville.
From the introduction:
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1.1 Who Should Read This Book?This book can be useful for a variety of readers, but we wrote it with two main target audiences in mind. One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is 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. Deep learning has already proven 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 has been organized into three parts in order 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 that are essentially solved technologies. Part III describes more speculative ideas that are widely believed to be important for future research in deep learning.
Readers should feel free to skip parts that are not relevant given their interests or background. Readers familiar with linear algebra, probability, and fundamental machine learning concepts can skip part I, for example, while readers who just want to implement a working system need not read beyond part II. To help choose which chapters to read, figure 1.6 provides a flowchart showing the high-level organization of the book.
We do assume that all readers come from a computer science background. We assume familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.
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This promises to be a real delight, whether read for an application space or to get a better handle on deep learning.