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Deep Learning (adaptiv - Hardcover, von Goodfellow Ian; Bengio - akzeptabel n-

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Deep Learning (Adaptive - Hardcover, by Goodfellow Ian; Bengio - Acceptable n
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Akzeptabel: Buch mit deutlichen Gebrauchsspuren. Der Einband kann einige Beschädigungen aufweisen, ...
Book Title
Deep Learning (Adaptive Computation and Machine Learning series)
ISBN
9780262035613
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
MIT Press
ISBN-10
0262035618
ISBN-13
9780262035613
eBay Product ID (ePID)
228981524

Product Key Features

Number of Pages
800 Pages
Language
English
Publication Name
Deep Learning
Publication Year
2016
Subject
Intelligence (Ai) & Semantics, Computer Science
Type
Textbook
Subject Area
Computers
Author
Yoshua Bengio, Ian Goodfellow, Aaron Courville
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover

Dimensions

Item Height
1.3 in
Item Weight
45.5 Oz
Item Length
9.3 in
Item Width
7.3 in

Additional Product Features

Intended Audience
Trade
LCCN
2016-022992
Reviews
[T]he AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology., [T]he AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology.-- Daniel D. Gutierrez , insideBIGDATA --
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
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., 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.
LC Classification Number
Q325.5.G66 2017

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  • von ldselvera
    01. Apr. 2020

    Sound book.

    Great book for anyone looking to learn deep learning. Has a very large section for background, in preparation for the actual deep learning material.

    Bestätigter Kauf: JaZustand: NeuVerkauft von: missyr70

  • von amartya.mitra
    19. Apr. 2021

    A heavy but interesting read! Must have for all DL aspirants!

    Amazing book for readers with slightly advanced introductory knowledge of Deep Learning or Machine Learning techniques. Leans slightly on the mathematical end but does provide a good discussion of exquisite collection of phenomena in DL.

    Bestätigter Kauf: JaZustand: Neu

  • von mp6d844
    01. Dez. 2023

    Good, QC issues

    Good book, however some graphs are missing and printing seems to be a little off, but still readable as a reference.

    Bestätigter Kauf: JaZustand: NeuVerkauft von: smileshop_3

  • von hans.chad
    19. Jun. 2018

    Lovely

    A great book ! I

    Bestätigter Kauf: JaZustand: NeuVerkauft von: textbooks_xpress

  • von 6aqnjbw3sgy@Deleted
    12. Nov. 2019

    excellent

    Excellent book to get started on ML

    Bestätigter Kauf: JaZustand: NeuVerkauft von: rI9ybzIeT8O@Deleted

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