Deep Learning with Python (Second Edition) Francois Chollet

Deep Learning (Adaptive Computation and Machine Learning series)
By Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This authoritative textbook is considered the definitive introduction to deep learning. Written by three leading experts in the field, it provides a clear and comprehensive foundation in both the theory and practice of modern machine learning methods.

The book begins with the mathematical and conceptual building blocks—linear algebra, probability, and numerical computation—before progressing to core machine learning concepts. It then explores deep learning techniques in depth, including feedforward networks, optimization algorithms, convolutional networks, sequence modeling, autoencoders, and generative models.

Later chapters cover advanced topics such as deep generative modeling, representation learning, structured probabilistic models, and the theoretical perspectives that drive cutting-edge research. The authors also discuss practical challenges like optimization difficulties, overfitting, and ethical considerations in deploying AI systems.

Used widely in both academia and industry, this text is ideal for students, researchers, and professionals who want to understand the principles of deep learning and apply them to fields like computer vision, natural language processing, robotics, and more.