Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems
2nd edition
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- Nombre de pages820
- PrésentationBroché
- FormatGrand Format
- Poids1.315 kg
- Dimensions18,0 cm × 23,0 cm × 3,1 cm
- ISBN978-1-4920-3264-9
- EAN9781492032649
- Date de parution01/10/2019
- ÉditeurO'Reilly
Résumé
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 …
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.
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.
- 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.
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.
- 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.
