Applied Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
99.99 €
Out of stock
Applied Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
99.99 €
Through a series of remarkable advances, deep learning has greatly enhanced the entire field of machine learning. Nowadays, even programmers who know almost nothing about this technology can use simple and efficient tools to implement programs that can learn from data. The new edition of the bestselling book, based on specific examples, minimum theories and ready-made production-level Python frameworks, will help you gain an intuitive understanding of the concepts and tools designed to build intelligent systems.
You will learn a wide range of techniques that can be quickly put into practice. Given the presence of exercises in each chapter to consolidate what you have learned, you only need programming experience to get started. All code is available on GitHub. It has been updated to accommodate TensorFlow 2 and the latest version of Scikit-Learn.
Features of the book:
- Learn the basics of machine learning on a cross-cutting project using Scikit-Learn and Pandas
- Build and train neural networks with multiple architectures for classification and regression using TensorFlow 2.
- Familiarize yourself with object identification, semantic segmentation, attention mechanisms, language models that generate adversarial networks, and more.
Explore Keras API - the official high-level API for TensorFlow 2
Launch the TensorFlow model using the TensorFlow Data API, distribution strategies, TF Transform and TF Serving.
- Deploy models on Google Cloud’s AI Platform or on mobile devices.
- Use non-teacher learning techniques such as dimension reduction, clustering, and anomaly detection.
- Create autonomous learning agents through reinforcement learning, including using the TF-Agents library;
You will learn a wide range of techniques that can be quickly put into practice. Given the presence of exercises in each chapter to consolidate what you have learned, you only need programming experience to get started. All code is available on GitHub. It has been updated to accommodate TensorFlow 2 and the latest version of Scikit-Learn.
Features of the book:
- Learn the basics of machine learning on a cross-cutting project using Scikit-Learn and Pandas
- Build and train neural networks with multiple architectures for classification and regression using TensorFlow 2.
- Familiarize yourself with object identification, semantic segmentation, attention mechanisms, language models that generate adversarial networks, and more.
Explore Keras API - the official high-level API for TensorFlow 2
Launch the TensorFlow model using the TensorFlow Data API, distribution strategies, TF Transform and TF Serving.
- Deploy models on Google Cloud’s AI Platform or on mobile devices.
- Use non-teacher learning techniques such as dimension reduction, clustering, and anomaly detection.
- Create autonomous learning agents through reinforcement learning, including using the TF-Agents library;
See also:
- All books by the publisher
- All books by the author