MARC details
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| fixed length control field |
02084 a2200241 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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231230b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789355429988 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 FOS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Foster, David |
| 245 ## - TITLE STATEMENT |
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| Title |
Generative deep learning: teaching machines to paint, write, compose, and play |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Mumbai: |
| Name of publisher, distributor, etc |
O'Reilly Media & Shroff Publishers & Distributors, |
| Date of publication, distribution, etc |
2023. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxvi, 426p.: |
| Other physical details |
pbk.: |
| Dimensions |
24cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes Index |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.<br/><br/>Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.<br/><br/>Discover how variational autoencoders can change facial expressions in photos<br/>Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation<br/>Create recurrent generative models for text generation and learn how to improve the models using attention<br/>Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting<br/>Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN<br/><br/>https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/ |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine Learning |
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Generative Adversarial Networks |
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Encoder-decoder Models |
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CycleGAN |
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ProGAN |
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| Topical term or geographic name as entry element |
StyleGAN |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
