Description

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.”Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to be told from experience and be mindful the world on the subject of a hierarchy of concepts. Because the computer gathers knowledge from experience, there’s no need for a human computer operator to formally specify all of the knowledge that the computer needs. The hierarchy of concepts allows the computer to be told complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep., This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames., In the end, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be utilized by undergraduate or graduate students planning careers in either industry or research, and by software engineers who need to begin the usage of deep learning in their products or platforms. A web page offers supplementary ma