MARC details
| 000 -LEADER |
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| fixed length control field |
02158aam a2200217 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
241011b2024 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031454677 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
B4D3 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Bishop, Christopher M. |
| 9 (RLIN) |
385679 |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep learning: foundations and concepts |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer |
| Date of publication, distribution, etc |
2024 |
| Place of publication, distribution, etc |
Cham |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 649 p. |
| Other physical details |
includes appendix, bibliography and index |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.<br/><br/>The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.<br/><br/>A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.<br/><br/><br/><br/>https://link.springer.com/book/10.1007/978-3-031-45468-4 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
machine learning |
| 9 (RLIN) |
57333 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Deep learning |
| 9 (RLIN) |
427610 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Neural networks |
| 9 (RLIN) |
138382 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Decision theory |
| 9 (RLIN) |
427611 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Directed graphical models |
| 9 (RLIN) |
427612 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Convolutional networks |
| 9 (RLIN) |
427613 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
