Deep Learning with Python (2nd Edition) – François Chollet

Deep Learning with Python (Second Edition)

Author: François Chollet
Publisher: Manning Publications
Format: Full-Color Hardcover / Paperback
Language: English

Book Description

Deep Learning with Python (2nd Edition) by François Chollet — the creator of Keras and a leading AI researcher at Google — is a hands-on guide to mastering modern deep learning techniques using Python and TensorFlow. This full-color edition blends intuitive explanations with practical coding examples, helping readers understand not just how deep learning works, but why it works.

The book walks through the core principles of neural networks, convolutional networks, recurrent networks, transformers, and generative models, balancing theory with real-world implementation. Each chapter introduces concepts step-by-step with working code samples you can run and adapt.

Key Features

• Practical, example-driven learning: Build and train models for real-world use cases such as image recognition, text generation, and time-series prediction.
• Comprehensive coverage: Learn fundamentals of machine learning plus advanced topics like deep generative learning, transfer learning, and self-supervised models.
• Modern frameworks: Uses TensorFlow 2.x and Keras for a seamless learning experience in Python.
• Full-color visuals: Includes clear illustrations, annotated code, and output examples for better understanding.
• Balanced theory and application: Understand both mathematical foundations and practical design patterns for deep learning architectures.

Who Should Read This Book

• Software engineers, data scientists, and students entering AI and machine learning.
• Developers looking to build scalable neural network models.
• Researchers seeking a modern, implementation-focused perspective on deep learning systems.

Benefits & Uses

• Gain a solid understanding of how to design, train, and deploy deep learning models.
• Apply deep learning to NLP, computer vision, recommendation systems, and AI research.
• Transition smoothly from beginner to expert with structured chapters and guided projects.
• Ideal for university courses, AI bootcamps, and professional self-study.