A hands-on and intuitive guide to the foundations of modern deep learning
In Deep Learning: Principles and Implementations, distinguished researcher and professor Weidong “Will” Kuang delivers an up-to-date exploration of how major deep learning algorithms and architectures are formalized and developed from mathematical equations. The book bridges theory and practice and covers a wide range of fundamental topics, including linear regression, logistic regression, basic neural networks, convolution neural networks, as well as other basic and advanced subjects in the field.
The author provides intuitive introductions to each subject and presents the development of algorithms and architectures from basic mathematical concepts. Along the way, he relies on straightforward math to keep the topics accessible for non-mathematicians and accompanies his explanations with tested Python sample code you can apply in your own work.
You’ll also find:
- Thorough introductions to both linear and logistic regression, offering a solid foundation and insight into neural networks
- Comprehensive explorations of neural networks, computer vision, natural language processing, generative models, and reinforcement learning
- Practical exercises that students and practitioners can use to apply and develop the concepts found in the book
- Balanced treatments of the mathematics, algorithms, architecture, and code that serve as the foundations of a complete understanding of deep learning
Perfect for undergraduate and graduate students with an interest in deep learning, Deep Learning: Principles and Implementations will also benefit practicing software engineers, faculty, and researchers whose work involves deep learning and related topics.
Table of Contents:
Preface
Mathematical notation
Chapter 1 Introduction to Deep Learning
Chapter 2 Linear Regression
Chapter 3 Classification and Logistic Regression
Chapter 4 Basics of Neural Networks
Chapter 5 Practical Considerations in Neural Networks
Chapter 6 Introduction to PyTorch
Chapter 7 Convolutional Neural Networks
Chapter 8 Classical Architectures of CNNs
Chapter 9 Object Detection – YOLO
Chapter 10 Introduction to Probabilistic Generative Models
Chapter 11 Generative Adversarial Networks
Chapter 12 Diffusion Models
Chapter 13 Word Embedding
Chapter 14 Recurrent Neural Networks
Chapter 15 Transformer
Chapter 16 Introduction to Reinforcement Learning
Chapter 17 Deep Q-Learning
Chapter 18 Policy Gradient Methods
Appendix A Mathematics in Machine Learning
Index
About the Author :
Weidong “Will” Kuang, PhD, is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Texas, Rio Grande Valley. He is an expert in signal processing, deep learning, and integrated circuits.
