Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems (Record no. 20356)

010 ## - ISBN
ISBN 9781492032649
200 ## - titre
titre Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems
auteur Aurélien, Géron
Type document Livre
205 ## - mention d'édition
mention d'édition 2e ed.
210 ## - éditeur
lieu de publication Sebastopol (Calif.)
nom de l'editeur O'Reilly Media
date de publication 2019
101 0# - langue
langue du document Anglais
langue de l'oeuvre originale Anglais
215 ## - description
Nombre de pages 1 vol. (xxv-820 p.)
format 24 cm
606 ## - sujets
sujet Machine learning
606 ## - sujets
sujet Intelligence artificielle
606 ## - sujets
sujet Apprentissage automatique
330 ## - résumé
texte de la note Through a series of breakthroughs, Deep Learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.<br/>You'll learn a range of techniques that you can quickly put to use. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. All code is available on GitHub. It has been updated to TensorFlow 2 and the latest version of Scikit-Learn. - Learn Machine Learning fundamentals through an end-to-end project using Scikit-Learn and pandas.<br/>- Build and train many neural network architectures for classification and regression using TensorFlow 2 ; - Discover object detection, semantic segmentation, attention mechanisms, language models, GANs, and more ; - Explore the Keras API, the official high-level API for TensorFlow 2 ; - Productionize TensorFlow models using TensorFlow's Data API, distribution strategies API, TF Transform, and TF-Serving ; - Deploy on Google Cloud AI Platform or on mobile devices ; - Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection ; - Create autonomous learning agents with Reinforcement Learning, including using the TF-Agents library.
686 ## - Classification
Classification 006.3 Intelligence artificielle - Machine Learning
700 ## - auteur(s)
auteur Géron
partie du nom autre que l'élément d';entrée Aurélien
koha internal code 29811
001 - notice numéro
Numéro d'identification notice 24025807X
009 - PPN
ppn 24025807X
100 ## - données générales de traitement
données générales de traitement 20191021h20192019k y0frey50 ba
105 ## - zone de données codées :textes, monogaphies
données codées - monographies a a 001yy
Propriétaire Dépositaire Localisation code barre cote Statut note
ENS Rennes - Bibliothèque ENS Rennes - Bibliothèque Informatique 041372 006.3 GER Empruntable 006.3 Intelligence artificielle - Machine Learning

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