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: August 16, 2022)- Chapter 1:
**Introduction**(Last update: August 16, 2022) - Chapter 2:
**Principles of Machine Learning**(Last update: August 16, 2022) - Chapter 3:
**Simple Neural Networks**(Last update: August 16, 2022) - Chapter 4:
**Optimization Algorithms**(New: August 16, 2022) - Chapter 5:
**Feed-Forward Deep Networks**(Expect draft on December 15, 2022) - Chapter 6:
**Convolutional Neural Networks**(Expect draft on February 15, 2022) - Chapter 7:
**Sequence Models**(Expect draft in mid 2023) - Chapter 8:
**Tricks of the Trade**(Expect draft in mid 2023) - Chapter 9:
**Generative Adversarial Networks**(Expect draft in late 2023) - Chapter 10:
**Deep Reinforcement Learning**(Expect draft in late 2023) - Appendix A:
**Some Multivariable Calculus**(Last update: August 16, 2022) - Appendix B:
**Cross Entropy and Other Expectations with Logarithms**(Expect draft in mid 2023) - Appendix C:
**Gaussian processes for Bayesian optimization**(Expect draft in mid 2023) **Back matter**(Last update: Augst 16, 2022)

**See also:
**

- Early material used in an AMSI summer school 2021 course.