Hands-On Machine Learning with Scikit-Learn and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems

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Through a series of recent 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. This practical book shows you how.By using concrete examples, minimal theory and two production-ready Python frameworks scikit-learn and TensorFlow author AurŽlien GŽron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.

Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-end
Explore several training models, including support vector machines, decision trees, random forests and ensemble methods
Use the TensorFlow library to build and train neural nets
Dive into neural net architectures, including convolutional nets, recurrent nets and deep reinforcement learning
Learn techniques for training and scaling deep neural nets
Apply practical code examples without acquiring excessive machine learning theory or algorithm details.

AuthorAurelien Geron
Height103 mm
Length900 mm
Width700 mm
Weight198 g
Language TypePublished
Number Of Pages568
Product GroupBook
Publication Date2017
Sales Rank3996

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