Geometry of Deep Learning: A Signal Processing Perspective (Mathematics in Industry, 37)

This book provides a comprehensive overview of deep learning techniques from a unified geometric perspective, offering a systematic understanding of the working mechanism behind these powerful algorithms.

With up-to-date information on both practical and theoretical aspects, it can be used as an advanced textbook in universities or as a reference source for researchers.

The book covers various forms of generative models from a unified geometric perspective, demonstrating that they arise from statistical distance-minimization problems.

Whether you're an engineering or mathematics student looking to gain a deeper understanding of deep learning or a researcher seeking the latest algorithms and their underlying principles, this book is an essential resource.