Free download and read ebooks online Hands-On Machine Learning With Scikit-Learn And TensorFlow - Techniques And Tools To Build Learning Machines by Aurélien Géron full book PDF ePUB Audiobook. Download a free ebooks and kindle audiobook Hands-On Machine Learning With Scikit-Learn And TensorFlow - Techniques And Tools To Build Learning Machines with ISBN id 9781491962299 full page with book summary. Aurélien Géron book PDF ePUB of Hands-On Machine Learning With Scikit-Learn And TensorFlow - Techniques And Tools To Build Learning Machines published by O'Reilly Media in 2017-04. Hands-On Machine Learning with Scikit-Learn and TensorFlow released with category Computers with total 566 pages.
Hands-On Machine Learning With Scikit-Learn And TensorFlow - Techniques And Tools To Build Learning Machines by Aurélien Géron free online open library
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 Aur�lien G�ron 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 nets Use 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
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