An edition of Deep Learning (2016)

Deep Learning

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  • 3.7 (3 ratings)
  • 90 Want to read
  • 10 Currently reading
  • 3 Have read

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Last edited by Drini
October 28, 2022 | History
An edition of Deep Learning (2016)

Deep Learning

  • 3.7 (3 ratings)
  • 90 Want to read
  • 10 Currently reading
  • 3 Have read

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

Publish Date
Language
English

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Previews available in: English

Edition Availability
Cover of: Deep Learning
Deep Learning
2017, MIT Press
in English
Cover of: Deep Learning
Deep Learning
3 January 2017, MIT Press
Hardcover
Cover of: Deep Learning
Deep Learning
2016, MIT Press
in English
Cover of: Deep Learning
Deep Learning
2016, MIT Press
in English
Cover of: Deep Learning
Deep Learning
2016, deeplearningbook.org
Web Book in English

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Book Details


The Physical Object

Format
Web Book

Edition Identifiers

Open Library
OL40220570M

Work Identifiers

Work ID
OL17801809W

Work Description

"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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