An introduction to a broad field in deep learning covering n cells and structures background, deep learning techniques used in industry and research perspectives. "Written by three experts in the field of Deep learning, this is definitively the only comprehensive book." —Elon Musk, co-chairman of OpenAI; Founder and CEO of Tesla and SpaceX Deep learning is a form of machine computing that enables computers to learn from experience and to understand caring principles of a concept in the world. A human computer operator need not formally describe all the information she sees on her computer, as it gathers from her experience of using computers. Concepts rules allow students to learn conceptually complex concepts starting from the simpler ones; a graph of these structures will be many layers deep. This book introduces a broad range of topics in deep learning. The text provides a background in the fields and structures covering consumption algebra, probability theory and related concepts in information logic, numerical computation, and machine use. It incorporates deep learning techniques used by devices in the industry, including deep feedforward networks, regularization, optimization operations, convolutional networks, sequence management and practical equipment; and explores applications such as natural language processing, speech recognition, computer vision, online recommended systems, bioinformatics and video games. Finally, the book presents research possibilities covering theoretical topics such as cost factor models, autoencoders, representative learning, buyer-finding models, Monte Carlo methods, partition function, approximation and deep generative models. Deep learning can be used by undergraduates planning a career in industry or research, and by software engineers targeting deep learning or looking to start using it on their platforms.