The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.In Deep Learning with Python, Third Edition youll discover:Deep learning from first principlesThe latest features of Keras 3A primer on JAX, PyTorch, and TensorFlowImage classification and image segmentationTime series forecastingLarge Language modelsText classification and machine translationText and image generationbuild your own GPT and diffusion models!Scaling and tuning modelsWith over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator Franois Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images.About the technologyIn less than a decade, deep learning has changed the worldtwice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator Franois Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.About the bookDeep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer.What's insideHands-on, code-first learningComprehensive, from basics to generative AIIntuitive and easy math explanationsExamples in Keras, PyTorch, JAX, and TensorFlowAbout the readerFor readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.About the authorFranois Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive on Keras8 Image classification9 ConvNet architecture patterns10 Interpreting what ConvNets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions
