Introduction to deep learning (Record no. 30513)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01138nam a2200181 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250710b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262039512 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 CHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Charniak, Eugene |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to deep learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Massachusetts: |
| Name of publisher, distributor, etc. | The MIT Press; |
| Date of publication, distribution, etc. | c2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 174p. |
| 500 ## - GENERAL NOTE | |
| General note | Includes index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | A project-based guide to the basics of deep learning.<br/>This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computational learning theory |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Suppress in OPAC | No |
| Koha item type | Book |
No items available.