| Titre : | Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems |
| Auteurs : | Aurélien Géron, Auteur |
| Type de document : | texte imprimé |
| Editeur : | Santa Rosa, CA [Etats-Unis] : O'Reilly, 2022 |
| ISBN/ISSN/EAN : | 978-1-09-812597-4 |
| Format : | 1 volume (xxv-834 pages) / illustrations en noir et en couleur, couverture illustrée / 24 cm |
| Note générale : | Bibliographie p. XXI-XXII. Notes bibliographiques en bas de pages. Index |
| Langues: | Anglais |
| Index. décimale : | Informatique - Statistiques |
| Catégories : |
[Archirès ] 062 Information - communication > Informatique > Informatique appliquée > Intelligence artificielle [Archirès ] 062 Information - communication > Informatique > Logiciel [Archirès ] 062 Information - communication > Informatique > Programmation informatique [Archirès ] 062 Information - communication > Informatique > Programmation informatique > Langage de programmation |
| Résumé : | "Through a recent 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. This bestselling book uses concrete examples, minimal theory, and production-ready Python framework (Scikit-Learn, Keras, and TensorFlow) to help you again an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with wimple linear regression and progressing to deep neural networks. Numerous code examples and exercices throughout the book help you apply what you've learned. Programming expeience is all you need to get started". [résumé de l'éditeur] |
| Note de contenu : |
Sommaire :
Part. I, The fundamentals of machine learning 1, The machine learning landscape 2, End-to-end machine learning project 3, Classification 4, Traning models 5, Support vector machines 6, Decision trees 7, Ensemble learning and random forests 8, Dimensionality reduction 9, Unsupervided learning techniques Part II, Neural networks and deep learning 10, Introduction to artificial neural networks with Keras 11, Training deep neural networks 12, Custom models and training with TensorFlow 13, Loading and preprocessing data with TensorFlow 14, Deep computer vision using convolutional neural networks 15, Processing sequences using RNNs and CNNs 16, Natural language processing with RNNs and attention 17, Autoencoders, GANs and diffusion models 18, Reinforcement learning 19, Training and deploying TensorFlow models at scale |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| 035838 | N.09/GER | Livres | CENTRE DE DOCUMENTATION | Salle de consultation | Sorti jusqu'au 05/01/2026 |
