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  <titleInfo>
    <title>Hands-on machine learning with Scikit-Learn, Keras and TensorFlow</title>
    <subTitle>concepts, tools, and techniques to build intelligent systems</subTitle>
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  <name type="personal">
    <namePart>Géron, Aurélien</namePart>
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    <dateIssued encoding="marc">2023</dateIssued>
    <copyrightDate encoding="marc">2023</copyrightDate>
    <edition>Third edition.</edition>
    <issuance>monographic</issuance>
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    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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    <extent>xxv, 834 pages : illustrations (chiefly color) ; 24 cm</extent>
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  <abstract>"Through a recent series of 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 best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started"--</abstract>
  <tableOfContents>The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Autoencoders, GANs, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale.</tableOfContents>
  <note type="statement of responsibility">Aurélien Géron.</note>
  <note>Previous editions: 2019, 2017.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <titleInfo>
      <title>TensorFlow</title>
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  <subject authority="lcsh">
    <topic>Python (Computer program language)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Machine learning</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
  </subject>
  <subject authority="rvm">
    <topic>Apprentissage automatique</topic>
  </subject>
  <subject authority="rvm">
    <topic>Python (Langage de programmation)</topic>
  </subject>
  <subject authority="rvm">
    <topic>Intelligence artificielle</topic>
  </subject>
  <subject authority="aat">
    <topic>artificial intelligence</topic>
  </subject>
  <subject authority="fast">
    <topic>Artificial intelligence</topic>
  </subject>
  <subject authority="fast">
    <topic>Machine learning</topic>
  </subject>
  <subject authority="fast">
    <topic>Python (Computer program language)</topic>
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  <identifier type="isbn">9781098125974</identifier>
  <identifier type="isbn">1098125975</identifier>
  <identifier type="lccn">2023549175</identifier>
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