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Deep Learning (Adaptive Computation a..., Bach, Francis
US $64.99
ApproximatelyS$ 83.45
Condition:
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
6 available
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Located in: Montgomery Illinois, United States
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Estimated between Thu, 4 Sep and Mon, 8 Sep
Returns:
30 days return. Seller pays for return shipping.
Coverage:
Read item description or contact seller for details. See all detailsSee all details on coverage
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Seller assumes all responsibility for this listing.
eBay item number:314739226183
Item specifics
- Condition
- Book Title
- Deep Learning (Adaptive Computation and Machine Learning Series)
- ISBN
- 0262035618
- EAN
- 9780262035613
- Release Title
- Deep Learning (Adaptive Computation and Machine Learning Series)
- Artist
- Bach, Francis
- Brand
- N/A
- Colour
- N/A
About this product
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
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
Item description from the seller
Seller feedback (275,367)
- 0***a (1431)- Feedback left by buyer.Past monthVerified purchaseVery good condition
- .***. (13)- Feedback left by buyer.Past monthVerified purchaseBook was sold at a STEAL and was in great condition when I got it. Super helpful with my research project!
- e***i (1)- Feedback left by buyer.Past monthVerified purchaseQuick shipping, condition great, haven't had time to read it yet.
Seller feedback (275,367)
- 0***a (1431)- Feedback left by buyer.Past monthVerified purchaseVery good condition
- .***. (13)- Feedback left by buyer.Past monthVerified purchaseBook was sold at a STEAL and was in great condition when I got it. Super helpful with my research project!
- e***i (1)- Feedback left by buyer.Past monthVerified purchaseQuick shipping, condition great, haven't had time to read it yet.