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 projet progresses.**

**Preface and Table of Contents**(Last update: May 17, 2022)- Chapter 1:
**Introduction**(Last update: April 10, 2022) - Chapter 2:
**Principles of Machine Learning**(Last update: April 27, 2022) - Chapter 3:
**Simple Neural Networks**(Draft not posted yet) - Chapter 4:
**Optimization Algorithms**(Draft not posted yet) - Chapter 5:
**Feed-Forward Deep Networks**(Draft not posted yet) - Chapter 6:
**Convolutional Neural Networks**(Draft not posted yet) - Chapter 7:
**Sequence Models**(Draft not posted yet) - Chapter 8:
**Tricks of the Trade**(Draft not posted yet) - Chapter 9:
**Generative Adversarial Networks**(Draft not posted yet) - Chapter 10:
**Deep Reinforcement Learning**(Draft not posted yet) - Appendix A:
**Some Multivariable Calculus**(New: May 17, 2022) - Appendix B:
**Cross Entropy and Other Expectations with Logarithms**(Draft not posted yet) - Appendix C:
**Gaussian processes for Bayesian optimization**(Draft not posted yet) **Back matter**(New: May 17, 2022)

**See also:
**

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