| 000 -LEADER |
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| fixed length control field |
02961 a2200229 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240827144848.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230802b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780262035613 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
ICTS-TIFR |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q325.5 .G66 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Goodfellow, Ian |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Name of publisher, distributor, etc. |
The MIT Press, |
| Place of publication, distribution, etc. |
Cambridge, Massachusetts: |
| Date of publication, distribution, etc. |
[c2016] |
| 300 ## - Physical Description |
|---|
| Pages: |
775 p. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1 Introduction<br/>I Applied Math and Machine Learning Basics<br/>2 Linear Algebra<br/>3 Probability and Information Theory<br/>4 Numerical Computation<br/>5 Machine Learning Basics<br/><br/>II Deep Networks; Modern Practices<br/>6 Deep Feedforward Networks<br/>7 Regularization for Deep Learning<br/>8 Optimization for Training<br/>9 Convolutional Networks<br/>10 Sequence Modeling: Recurrent and Recursive Nets<br/>11 Practical Methodology<br/>12 Applications<br/><br/>III Deep Learning Research Factor Models<br/>13 Linear Factor Models<br/>14 Autoencoders<br/>15 Representation Learning<br/>16 Structured Probabilistic Models for Deep Learning<br/>17 Monte Carlo Methods<br/>18 Confronting the Partition Function<br/>19 Approximate Interface<br/>20 Deep Generative Models<br/><br/><br/> |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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.<br/><br/>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.<br/><br/>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. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Bengio, Yoshua |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Courville, Aaron |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Book |
