Description

This book provides a comprehensive exploration of both classical and modern models in deep learning, with a primary focus on the theory and algorithms that underpin the field. It delves into the fundamental concepts of neural networks, helping readers understand the key principles that guide the design and application of neural architectures across various domains. Central to the book are critical questions such as: Why do neural networks work? When do they outperform traditional machine-learning models? What makes depth in networks useful, and what are the challenges in training them? It also examines the pitfalls and complexities involved in deep learning, providing insights into the nuances of building