An uncompromising mathematical treatment of deep neural networks and generalization bounds.
1. Linear Algebra in Infinite Dimensions 2. Concentration Inequalities 3. Empirical Risk Minimization 4. Stochastic Gradient Dynamics 5. Overparameterization & Double Descent 6. The Neural Tangent Kernel
| Title | Mathematical Foundations of Neural Computing |
|---|---|
| ISBN-13 | 9781108429115 |
| ISBN-10 | 1108429112 |
| Publisher / Imprint | Apex Technical Publications |
| Binding | Hardcover with Matte Finish |
| Edition | 1st Edition |
| Publication Date | 2025-03-01 |
| Number of Pages | 520 |
| Weight | 900 grams |
| Dimensions | 17.0 x 3.5 x 24.5 cm |
| SKU | APX-MATH-006 |
Principal Scientist at Silicon Systems and Professor of Computing Systems. Pioneer in fault-tolerant distributed consensus protocols.
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