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  <titleInfo>
    <title>Deep learning generalization</title>
    <subTitle>theoretical foundations and practical strategies</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Liu, Peng</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="text">Boca Raton</placeTerm>
    </place>
    <publisher>CRC Press</publisher>
    <dateIssued>2026</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>ix, 220 p.</extent>
  </physicalDescription>
  <abstract>This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.</abstract>
  <note type="statement of responsibility">Peng Liu</note>
  <note>Includes bibliographical references and index.</note>
  <note>Eng</note>
  <subject>
    <topic>Machine learning</topic>
  </subject>
  <subject>
    <topic>Data mining</topic>
  </subject>
  <subject>
    <topic>Deep learning</topic>
  </subject>
  <classification authority="ddc">006.31 PEN-D</classification>
  <identifier type="isbn">9781032841892</identifier>
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    <recordCreationDate encoding="marc">260608</recordCreationDate>
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