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Math and Architectures of Deep Learning Krishnendu Chaudhury
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Math and Architectures of Deep Learning
Krishnendu Chaudhury
Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners.
Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
450 pages
| メディア | 書籍 Paperback Book (ソフトカバーで背表紙を接着した本) |
| リリース済み | 2024年3月15日 |
| ISBN13 | 9781617296482 |
| 出版社 | Manning Publications |
| ページ数 | 450 |
| 寸法 | 234 × 187 × 34 mm · 996 g |
| 言語 | 英語 |
