Deep learning with Python

Deep learning with Python

En 4e de couverture : "Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow p...

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Détails bibliographiques
Egalement en ligne : En ligne Via Deep learning with Python
Auteur principal : Chollet François (Auteur)
Format : Livre
Langue : anglais
Titre complet : Deep learning with Python / François Chollet
Édition : Second edition
Publié : Shelter Island (N. Y.) : Manning Publications , C 2021
Description matérielle : 1 vol. (XXIV-478 p.)
Sujets :
Python (langage de programmation)
Apprentissage automatique
Apprentissage profond
Réseaux neuronaux (informatique)
Documents associés : Autre format: Deep learning with Python
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320 |a Index 
330 |a En 4e de couverture : "Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach-- even if you have no background in mathematics or data science. This book shows you how to get started. "Deep learning with Python, second edition" introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you'll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You'll quickly pick up the skills you need to start developing deep-learning applications." 
359 2 |b 1. What is deep learning?  |b 2. The mathematical building blocks of neural networks  |b 3. Introduction to Keras and TensorFlow  |b 4. Getting started with neural networks: classification and regression  |b 5. Fundamentals of machine learning  |b 6. The universal workflow of machine learning  |b 7. Working with Keras: a deep dive  |b 8. Introduction to deep learning for computer vision  |b 9. Advanced deep learning for computer vision  |b 10. Deep learning for timeseries  |b 11. Deep learning for text  |b 12. Generative deep learning  |b 13. Best practices for the real world  |b 14. Conclusions 
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