| Titre : |
Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 |
| Type de document : |
texte imprimé |
| Auteurs : |
Sebastian Raschka, Auteur ; Vahid Mirjalili, Auteur |
| Mention d'édition : |
Third edition |
| Editeur : |
Birmingham : Packt Publishing |
| Année de publication : |
2019 |
| Importance : |
1 vol. (XXI-741 p.) |
| Présentation : |
graph. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-78995-575-0 |
| Langues : |
Anglais (eng) |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learningwith Python. It acts as both a clear step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.
This new third edition is updated for TensorFlow 2 and the latest additions to scikit-learn. It's expanded to cover two cutting-edge machine learning techniques : reinforcement learning and generative adversarial networks (GANs). This book is your companion, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn : Master the frameworks, models, and techniques that enable machines to 'learn' from data ; Use scikit-learn for machine learning and TensorFlow for deep learning ; Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more ; Build and train neural networks, GANs, and other models Add machine intelligence to web applications ; Clean and prepare data for machine learning ; Classify images using deep convolutional neural networks ; Understand best practices for evaluating and tuning models ; Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering ; Dig deeper into textual and social media data using sentiment analysis. |
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Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 [texte imprimé] / Sebastian Raschka, Auteur ; Vahid Mirjalili, Auteur . - Third edition . - Birmingham : Packt Publishing, 2019 . - 1 vol. (XXI-741 p.) : graph. ; 24 cm. ISBN : 978-1-78995-575-0 Langues : Anglais ( eng)
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learningwith Python. It acts as both a clear step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.
This new third edition is updated for TensorFlow 2 and the latest additions to scikit-learn. It's expanded to cover two cutting-edge machine learning techniques : reinforcement learning and generative adversarial networks (GANs). This book is your companion, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn : Master the frameworks, models, and techniques that enable machines to 'learn' from data ; Use scikit-learn for machine learning and TensorFlow for deep learning ; Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more ; Build and train neural networks, GANs, and other models Add machine intelligence to web applications ; Clean and prepare data for machine learning ; Classify images using deep convolutional neural networks ; Understand best practices for evaluating and tuning models ; Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering ; Dig deeper into textual and social media data using sentiment analysis. |
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./index.php?lvl=notice_display&id=103938 |
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Python machine learning
