Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went 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. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras 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 applications 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. What's Inside <ul>
<li>Deep learning from first principles</li>
<li>Setting up your own deep-learning environment </li>
<li>Image-classification models</li>
<li>Deep learning for text and sequences</li>
<li>Neural style transfer, text generation, and image generation</li>
</ul>
 About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents <ol>PART 1 - FUNDAMENTALS OF DEEP LEARNING <li>What is deep learning?</li>
<li>Before we begin: the mathematical building blocks of neural networks </li>
<li>Getting started with neural networks</li>
<li>Fundamentals of machine learning</li>PART 2 - DEEP LEARNING IN PRACTICE<li>Deep learning for computer vision</li>
<li>Deep learning for text and sequences</li>
<li>Advanced deep-learning best practices</li>
<li>Generative deep learning</li>
<li>Conclusions</li>
<li>appendix A - Installing Keras and its dependencies on Ubuntu</li>
<li>appendix B - Running Jupyter notebooks on an EC2 GPU instance</li>
</ol>

Deep Learning with Python

Computers & Technology

CHOLLET, François

Computer Science

Manning

Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside <ul>
<li>Deep learning from first principles</li>
<li>Setting up your own deep-learning environment</li>
<li>Image classification and generation</li>
<li>Deep learning for text and sequences</li>
</ul>
 About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents <ol>PART 1 - FUNDAMENTALS OF DEEP LEARNING<li>What is deep learning?</li>
<li>Before we begin: the mathematical building blocks of neural networks</li>
<li>Getting started with neural networks</li>
<li>Fundamentals of machine learning</li>PART 2 - DEEP LEARNING IN PRACTICE<li>Deep learning for computer vision</li>
<li>Deep learning for text and sequences</li>
<li>Advanced deep-learning best practices</li>
<li>Generative deep learning</li>
<li>Conclusions</li>
</ol>

Deep Learning with R

"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technology Machine learning has made remarkable progress in recent years. We've gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications 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. what's inside Deep learning from first principles Image-classification, imagine segmentation, and object detection Deep learning for natural language processing Timeseries forecasting Neural style transfer, text generation, and image generation about the reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Deep Learning with Python, Second Edition

Deep learning from the ground up using R and the powerful Keras library!\nIn Deep Learning with R, Second Edition you will learn:\nDeep learning from first principles 
Image classification and image segmentation 
Time series forecasting 
Text classification and machine translation 
Text generation, neural style transfer, and image generation\nDeep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.\nPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\nAbout the technology 
Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.\nAbout the book 
Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.\nWhat's inside\nImage classification and image segmentation 
Time series forecasting 
Text classification and machine translation 
Text generation, neural style transfer, and image generation\nAbout the reader 
For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.\nAbout the author 
François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book.\nTable of Contents 
1 What is deep learning? 
2 The mathematical building blocks of neural networks 
3 Introduction to Keras and TensorFlow 
4 Getting started with neural networks: Classification and regression 
5 Fundamentals of machine learning 
6 The universal workflow of machine learning 
7 Working with Keras: A deep dive 
8 Introduction to deep learning for computer vision 
9 Advanced deep learning for computer vision 
10 Deep learning for time series 
11 Deep learning for text 
12 Generative deep learning 
13 Best practices for the real world 
14 Conclusions

Deep Learning with R, Second Edition

Kalinowski, Tomasz

Allaire, J.J.

Deep learning avec JavaScript

Bileschi, Stanley

Cai, Shanqing

Nielsen, Eric D.

Programming

FIRST INTERACT

L’apprentissage automatique a fait des progrès remarquables au cours des dernières années. Nous sommes passés d’une reconnaissance quasi inutilisable de la parole et des images à une précision quasi humaine, de machines qui ne pouvaient pas battre un joueur de Go un peu expérimenté à la défaite d’un champion du monde. Derrière ces progrès se cache l’apprentissage profond ― une combinaison d’avancées théoriques et pratiques qui permet une multitude d’applications intelligentes jusque-là impossibles à réaliser.\nL’apprentissage profond avec Python présente le domaine de l’apprentissage profond en utilisant le langage Python et la puissante bibliothèque Keras. Rédigé par François Chollet, créateur de Keras et chercheur en intelligence artificielle à Google, cet ouvrage construit votre compréhension de l’apprentissage profond grâce à des explications intuitives et des exemples pratiques. Vous explorerez des concepts et des pratiques stimulantes avec des applications de vision par ordinateur, de traitement du langage naturel et de modèles génératifs. Lorsque vous aurez terminé, vous aurez les connaissances et les compétences pratiques nécessaires pour appliquer l’apprentissage profond à vos propres projets.\nAucune expérience préalable de Keras, de TensorFlow ou d’apprentissage automatique n’est requise. Les lecteurs ont besoin de compétences Python intermédiaires.\nLe livre est imprimé sur du papier certifié PEFC (Programme for the Endorsement of Forest Certification), les encres et techniques d'impression ont le label Imprim'Vert. Le Livre est imprimé en Région Centre-Val de Loire.\nUne compensation volontaire carbone de 3kg par ouvrage (pesant 1,250kg) est versée à la Fondation GoodPlanet.org. Machinelearning.fr s'engage pour la transition environnementale.

L'apprentissage profond avec Python

Reference

MACHINELEARNING

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