of Deep Learning

**Synopsis: ** This book provides a complete and concise overview of the mathematical engineering of deep learning. In addition to overviewing deep learning foundations, the treatment includes convolutional neural networks, recurrent neural networks, transformers, generative adversarial networks, reinforcement learning, and multiple tricks of the trade.
The focus is on the basic mathematical description of deep learning models, algorithms and methods. The presentation is mostly agnostic to computer code, neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such an approach is that a mathematically equipped reader can quickly grasp the essence of modern deep learning algorithms, models, and techniques without having to look at computer code, neuroscience, or the historical progression.

Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from the fields of engineering, signal processing, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

**Draft book chapters will be made available as the project progresses.**

**Preface and Table of Contents**(Last update: May 10, 2023)- Chapter 1:
**Introduction**(Last update: May 1, 2023) - Chapter 2:
**Principles of Machine Learning**(Last update: May 1, 2023) - Chapter 3:
**Simple Neural Networks**(Last update: December May 1, 2023) - Chapter 4:
**Optimization Algorithms**(Last update: May 1, 2023) - Chapter 5:
**Feed-Forward Deep Networks**(Last update: May 1, 2023) - Chapter 6:
**Convolutional Neural Networks**(New: May 1, 2023) - Chapter 7:
**Sequence Models**(Expect draft: June 15, 2023) - Chapter 8:
**Specialized Architectures and Paradigms**(Expect draft on October 15, 2023) -
**Epilogue**(Expect draft on October 15, 2023) - Appendix A:
**Some Multivariable Calculus**(Last update: May 1, 2023) - Appendix B:
**Cross Entropy and Other Expectations with Logarithms**(Last update: May 1, 2023) **Back matter**(Last update: May 1, 2023)

**Related courses and workshops:**

**See also:**

- Source code used to create figures and tables of the book. This includes code in Julia, Python, R, and TikZ.