Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools

Deep learning has revolutionized machine learning, making it accessible to programmers with minimal theory and two production-ready Python frameworks. This book provides concrete examples, minimal theory, and exercises to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Format: Paperback / softback
Length: 600 pages
Publication date: 14 October 2019
Publisher: O'Reilly Media, Inc, USA

Deep learning has revolutionized the field of machine learning, enabling programmers with limited knowledge to implement programs capable of learning from data. This practical book, authored by Aurelien Geron, provides a comprehensive guide to building intelligent systems using concrete examples, minimal theory, and two production-ready Python frameworks: Scikit-Learn and TensorFlow. With exercises in each chapter, beginners can gain an intuitive understanding of the concepts and tools for building intelligent systems. The book covers a range of techniques, from simple linear regression to deep neural networks, and includes hands-on exercises to apply the learned concepts. It also explores the machine learning landscape, particularly neural nets, and demonstrates how to use Scikit-Learn to track a machine-learning project end-to-end. Additionally, the book covers various training models, such as support vector machines, decision trees, random forests, and ensemble methods, using the TensorFlow library. It delves into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning, and provides techniques for training and scaling deep neural nets. Whether you are a programmer or a data scientist, this book is a valuable resource for anyone interested in leveraging the power of deep learning.

Weight: 1288g
Dimension: 177 x 232 x 33 (mm)
ISBN-13: 9781492032649
Edition number: 2 New edition