Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
3rd edition
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- Nombre de pages741
- PrésentationBroché
- FormatGrand Format
- Poids1.41 kg
- Dimensions19,1 cm × 23,5 cm × 4,0 cm
- ISBN978-1-78995-575-0
- EAN9781789955750
- Date de parution09/12/2019
- CollectionExpert Insight
- ÉditeurPackt Publishing
Résumé
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.
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.