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"Machine learning has made remarkable progress in recent years. We wen from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning - a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. [This book] introduces the field of deep learning using the Python language and the powerful Kears library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with appplications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects." 4ème de couverture

Part 1. Fundamentals of deep learning 1. What is deep learning ? 2. Before we begin : the mathematical building blocks of neural networks 3. Getting started with neural networks 4. Fundamentals of machine learning. Part 2. Deep learning in practice 5. Deep learning for computer vision 6. Deep learning for text and sequences 7. Advanced deep-learning best practices 8. Generative deep learning 9. Conclusions