Mathematical Foundations of Neural Computing

Convex Optimization, High-Dimensional Probability & Gradient Dynamics

By Prof. Marcus Chen

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Synopsis Overview

An uncompromising mathematical treatment of deep neural networks and generalization bounds.

ISBN-13: 9781108429115

Binding: Hardcover with Matte Finish

Pages: 520 pages

Language: English

              Co-authored by Prof. Marcus Chen and leading mathematicians, this text provides proofs for loss landscape convexity, non-convex convergence rates, Neural Tangent Kernel theory, and PAC-Bayesian bounds.
            

                An uncompromising mathematical treatment of deep neural networks and generalization bounds.
              

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

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