Geometry of Deep Learning: A Signal Processing Perspective (Mathematics in Industry)

This book provides a comprehensive overview of deep learning, offering insights into its underlying geometry and the latest tools and techniques in the field.

With a focus on understanding the working mechanism of deep learning from a unified geometric perspective, this book covers classical kernel machine learning approaches, attention, normalization, Transformer, BERT, GPT-3, and other heuristic approaches.

The author offers a unified geometric analysis to understand the working mechanism of deep learning and describes different forms of generative models from a geometric perspective.

This book is an advanced deep learning textbook, a reference source for researchers, and a codeshare course for engineering and mathematics students.

It contains up-to-date information from both a practical and theoretical point of view, making it an essential resource for anyone interested in acquiring the latest deep learning algorithms and their underlying principles.