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

Aurelien Geron

  • Bindwijze: Paperback
  • Taal: en
  • Categorie: Computers & Informatica
  • ISBN: 9781492032649
Concepts, Tools, and Techniques to Build Intelligent Systems
Inhoud
Taal:en
Bindwijze:Paperback
Oorspronkelijke releasedatum:11 oktober 2019
Aantal pagina's:600
Illustraties:Nee
Betrokkenen
Hoofdauteur:Aurelien Geron
Hoofdauteur:Aurelien Geron
Overige kenmerken
Editie:2nd New edition
Extra groot lettertype:Nee
Studieboek:Ja
Verpakking breedte:180 mm
Verpakking hoogte:33 mm
Verpakking lengte:233 mm
Verpakkingsgewicht:1342 g
Overige kenmerken
Editie:2nd New edition
Extra groot lettertype:Nee
Studieboek:Ja
Verpakking breedte:180 mm
Verpakking hoogte:33 mm
Verpakking lengte:233 mm
Verpakkingsgewicht:1342 g

Samenvatting

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 Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youll 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 youve 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