Deep Learning (Adaptive Computation and Machine Learning series)

4
ISBN-13: 9780262035613
ISBN-10: 0262035618
Author: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Publication date: 2016
Publisher: The MIT Press
Format: Hardcover 800 pages
Category: Computers
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Book details

ISBN-13: 9780262035613
ISBN-10: 0262035618
Author: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Publication date: 2016
Publisher: The MIT Press
Format: Hardcover 800 pages
Category: Computers

Summary

Acknowledged authors Ian Goodfellow , Yoshua Bengio , Aaron Courville wrote Deep Learning (Adaptive Computation and Machine Learning series) comprising 800 pages back in 2016. Textbook and eTextbook are published under ISBN 0262035618 and 9780262035613. Since then Deep Learning (Adaptive Computation and Machine Learning series) textbook received total rating of 4 stars and was available to sell back to BooksRun online for the top buyback price of $ 21.39 or rent at the marketplace.

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 SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn 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. Finally, 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 used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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