MARC details
| 000 -LEADER |
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| fixed length control field |
02286 a2200277 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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230713b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789355420121 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
BUD |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Buduma, Nithin |
| 245 ## - TITLE STATEMENT |
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| Title |
Fundamentals of deep learning: designing next generation machine intelligence algorithms |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Beijing: |
| Name of publisher, distributor, etc |
O'Reilly Media, |
| Date of publication, distribution, etc |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiii, 372p.: |
| Other physical details |
ill; pbk: |
| Dimensions |
23cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.<br/>The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.<br/>-Learn the mathematics behind machine learning jargon<br/>-Examine the foundations of machine learning and neural networks<br/>-Manage problems that arise as you begin to make networks deeper<br/>-Build neural networks that analyze complex images<br/>-Perform effective dimensionality reduction using autoencoders<br/>-Dive deep into sequence analysis to examine language<br/>-Explore methods in interpreting complex machine learning models<br/>-Gain theoretical and practical knowledge on generative modeling<br/>-Understand the fundamentals of reinforcement learning<br/><br/>https://www.oreilly.com/library/view/fundamentals-of-deep/9781492082170/ |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Deep learning (Machine learning) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning--Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural networks (Computer science)--Models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
PyTorch |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Building intelligent machines |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Buduma, Nikhil |
| Relator term |
Co-author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Papa, Joe |
| Relator term |
Co-author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
