Algorithmic Foundations of Deep Learning - Eric Howard

Algorithmic Foundations of Deep Learning - Eric Howard
English | December 31, 2025 | ASIN: B0GDDY7XQB | 317 pages | PDF | 5.52 MB
Algorithmic Foundations of Deep Learning presents a mathematically rigorous and conceptually unified treatment of the core structures, algorithms, and theoretical frameworks that underlie contemporary deep learning systems. Structured as an advanced technical monograph, the book moves systematically from foundational linear algebra, probability theory, and optimization to the construction, training, and analysis of neural architectures used in supervised, unsupervised, and generative learning. Emphasizing precision and generality over tutorial exposition, the text develops the formal underpinnings of gradient-based learning, expressive capacity, architectural design, and generalization behavior. Detailed chapters are devoted to convolutional and recurrent architectures, spatial-frequency duality, Fourier and pooling operators, depth-efficient stacking, weight sharing, and architectural sparsity. Later sections extend the analysis to generative adversarial systems, self-supervised pretraining regimes, and stability bounds for parameter-efficient networks. Beyond the algorithmic machinery, the volume engages deeply with questions of regularization, convergence, and optimization dynamics, providing formal analyses of training regimes, spectral characteristics of network layers, and the geometric and statistical structure of learned representations. This book is intended for researchers, postgraduate students, and practitioners in machine learning, applied mathematics, theoretical computer science, and signal processing who seek a rigorous and exhaustive treatment of deep learning beyond implementation details or conceptual overviews. It assumes a high degree of mathematical maturity and is positioned as a reference work rather than an introductory textbook.