Deep Learning (Record no. 6230)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03338nam a22002057a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221019122551.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221019b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262035613 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | GOO |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Goodfellow, Ian |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | Massachusetts Institute of Technology |
| Date of publication, distribution, etc. | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Page No. | xxii, 775p |
| Accompanying material | Hardcover |
| 500 ## - GENERAL NOTE | |
| General note | 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.<br/>“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”<br/>—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX<br/><br/>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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Table of Contents<br/>Acknowledgements<br/>Notation<br/>1 Introduction<br/>Part 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/>Part II: Modern Practical Deep Networks<br/>6 Deep Feedforward Networks<br/>7 Regularization for Deep Learning<br/>8 Optimization for Training Deep Models<br/>9 Convolutional Networks<br/>10 Sequence Modeling: Recurrent and Recursive Nets<br/>11 Practical Methodology<br/>12 Applications<br/>Part III: Deep Learning Research<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 Inference<br/>20 Deep Generative Models<br/>Bibliography<br/>Index<br/> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bengio, Yoshua |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Courville, Aaron |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Reference |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Bill Date | Koha item type | Bill Number | Collection code | Currency | Cost, normal purchase price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Raj Kumar Goel Institute of Technology | Raj Kumar Goel Institute of Technology | 19/10/2022 | 006.31 GOO | 89196 | 19/10/2022 | Reference | 22-23/CRB/248 | |||||||||
| Dewey Decimal Classification | Raj Kumar Goel Institute of Technology | Raj Kumar Goel Institute of Technology | 09/09/2025 | 006.31 GOO | 97258 | 18/09/2025 | 08/09/2025 | Reference | BOS-000501 | DS (Data Science) | ₹ | 8860.00 | |||||
| Dewey Decimal Classification | Raj Kumar Goel Institute of Technology | Raj Kumar Goel Institute of Technology | 09/09/2025 | 006.31 GOO | 97259 | 18/09/2025 | 08/09/2025 | Reference | BOS-000501 | DS (Data Science) | ₹ | 8860.00 |