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Titre : Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems Type de document : texte imprimé Auteurs : Aurélien Géron, Auteur Mention d'édition : 2nd ed Editeur : Sebastopol [Etats-Unis] : O'Reilly Media Année de publication : 2019 Importance : XXV, 819 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-4920-3264-9 Note générale : Appendix. Index Langues : Anglais (eng) Mots-clés : Apprentissage automatique
Intelligence artificielle
Machine learning
Artificial intelligenceIndex. décimale : 004.8 Intelligence artificielle Note de contenu : Summary :
Part I. The fundamentals of machine learning.
1. The machine learning landscape.
2. End-to-end machine learning project.
3. Classification.
4. Training models.
5. Support vector machine.
6. Decision trees.
7. Ensemble learning and random forests.
8. Dimensionality reduction.
9. Unsupervised 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. Representation Learning and Generative Learning Using Autoencoders and GANs.
18.Reinforcement Learning.
19. Training and Deploying TensorFlow Models at Scale.Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems [texte imprimé] / Aurélien Géron, Auteur . - 2nd ed . - Sebastopol [Etats-Unis] : O'Reilly Media, 2019 . - XXV, 819 p. : ill. ; 24 cm.
ISBN : 978-1-4920-3264-9
Appendix. Index
Langues : Anglais (eng)
Mots-clés : Apprentissage automatique
Intelligence artificielle
Machine learning
Artificial intelligenceIndex. décimale : 004.8 Intelligence artificielle Note de contenu : Summary :
Part I. The fundamentals of machine learning.
1. The machine learning landscape.
2. End-to-end machine learning project.
3. Classification.
4. Training models.
5. Support vector machine.
6. Decision trees.
7. Ensemble learning and random forests.
8. Dimensionality reduction.
9. Unsupervised 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. Representation Learning and Generative Learning Using Autoencoders and GANs.
18.Reinforcement Learning.
19. Training and Deploying TensorFlow Models at Scale.Réservation
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Titre : Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps Type de document : texte imprimé Auteurs : Valliappa Lakshmanan, Auteur ; Sara Robinson, Auteur ; Michael Munn, Auteur Editeur : Sebastopol [Etats-Unis] : O'Reilly Media Année de publication : 2021 Importance : XIV, 390 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-09-811578-4 Note générale : Index Langues : Anglais (eng) Mots-clés : Machine learning
Computer programming
Big data
Apprentissage automatique
Design patternsIndex. décimale : 004.8 Intelligence artificielle Résumé : n this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. Note de contenu : Summary :
1. The need for machine learning design patterns.
2. Data representation design patterns.
3. Problem representation design patterns.
4. Model training patterns.
5. Design patterns for resilient serving.
6. Reproducibility design patterns.
7. Responsible AI.
8. Connected patterns.Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps [texte imprimé] / Valliappa Lakshmanan, Auteur ; Sara Robinson, Auteur ; Michael Munn, Auteur . - Sebastopol [Etats-Unis] : O'Reilly Media, 2021 . - XIV, 390 p. : ill. ; 24 cm.
ISBN : 978-1-09-811578-4
Index
Langues : Anglais (eng)
Mots-clés : Machine learning
Computer programming
Big data
Apprentissage automatique
Design patternsIndex. décimale : 004.8 Intelligence artificielle Résumé : n this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. Note de contenu : Summary :
1. The need for machine learning design patterns.
2. Data representation design patterns.
3. Problem representation design patterns.
4. Model training patterns.
5. Design patterns for resilient serving.
6. Reproducibility design patterns.
7. Responsible AI.
8. Connected patterns.Réservation
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Titre : Natural language processing with Python Type de document : texte imprimé Auteurs : Steven Bird, Auteur ; Ewan Klein, Auteur ; Edward Loper, Auteur Editeur : Sebastopol [Etats-Unis] : O'Reilly Media Année de publication : 2009 Importance : XX, 479 p. Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-0-596-51649-9 Note générale : La couv. porte en plus : "Analyzing text with the natural language toolkit".
Bibliogr. p. 449-458. IndexLangues : Anglais (eng) Mots-clés : Traitement automatique du langage naturel
Python (langage de programmation)Index. décimale : 004.43 Langage de programmation Résumé : This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Note de contenu : Summary :
1. Language processing and Python.
2. Accessing text corpora and lexical resources.
3. Processing raw text.
4. Writing structured programs.
5. Categorizing and tagging words.
6. Learning to classify text.
7. Extracting information from text.
8. Analyzing sentence structure.
9. Building feature-based grammars.
...Natural language processing with Python [texte imprimé] / Steven Bird, Auteur ; Ewan Klein, Auteur ; Edward Loper, Auteur . - Sebastopol [Etats-Unis] : O'Reilly Media, 2009 . - XX, 479 p. : ill. ; 23 cm.
ISBN : 978-0-596-51649-9
La couv. porte en plus : "Analyzing text with the natural language toolkit".
Bibliogr. p. 449-458. Index
Langues : Anglais (eng)
Mots-clés : Traitement automatique du langage naturel
Python (langage de programmation)Index. décimale : 004.43 Langage de programmation Résumé : This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Note de contenu : Summary :
1. Language processing and Python.
2. Accessing text corpora and lexical resources.
3. Processing raw text.
4. Writing structured programs.
5. Categorizing and tagging words.
6. Learning to classify text.
7. Extracting information from text.
8. Analyzing sentence structure.
9. Building feature-based grammars.
...Réservation
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