Neural Networks and Deep Learning

Focusing on the theory and algorithms of deep learning, this book delves into the intricacies of neural networks, addressing critical questions about their functionality and effectiveness compared to traditional machine-learning models. It explores the challenges of training these networks and highlights common pitfalls. Rich in practical applications, the text covers diverse fields such as recommender systems, machine translation, image classification, and reinforcement learning, providing insights into the design of neural architectures tailored to various problems. The chapters are organized into three distinct categories for structured learning.