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Deep learning is a type of machine learning that gives computers the ability to interpret the world in terms of a hierarchy of concepts and learn from experience.

There is no need for a human computer operator to expressly specify all the knowledge that the computer needs because the computer learns through experience. The concept hierarchy enables the computer to learn complex concepts by constructing them from smaller ones; a graph representing these hierarchies would have several levels. This book introduces a wide range of deep-learning topics.

The text provides the mathematical and conceptual basis, covering pertinent ideas in numerical computation, machine learning, probability theory, and linear algebra. It examines applications like natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games and describes deep learning techniques used by practitioners in industry, such as deep feedforward networks, regularization, optimization algorithms, convolutional networks, and practical methodology.

The book concludes by providing research perspectives on a variety of theoretical subjects, including deep generative models, autoencoders, representation learning, structured probabilistic models, Monte Carlo approaches, and partition functions.

"Deep Learning is the only comprehensive book on the subject, written by three leading experts in the area." — Elon Musk, co-founder and CEO of Tesla and SpaceX as well as co-chair of OpenAI.