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  <titleInfo>
    <title>Deep learning: techniques and models / by S. Rethinavalli, and S. Hareesh</title>
  </titleInfo>
  <name type="personal">
    <namePart>Rethinavalli, S.</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Hareesh, S.</namePart>
  </name>
  <typeOfResource/>
  <originInfo>
    <place>
      <placeTerm type="text">Chennai</placeTerm>
    </place>
    <publisher>National Academic Press</publisher>
    <dateIssued>2026</dateIssued>
    <edition>1st edition.</edition>
    <issuance/>
  </originInfo>
  <physicalDescription>
    <extent>vi, 186 p. 23.5 cm.</extent>
  </physicalDescription>
  <abstract>Deep Learning: Techniques and Models offers a comprehensive introduction to the rapidly evolving world of artificial intelligence and neural networks. The book explores core concepts such as convolutional and recurrent neural networks, deep belief systems, generative models, and reinforcement learning. It emphasizes both theoretical understanding and practical implementation, making it an ideal guide for students, researchers, and professionals in computer science and data analytics. With clear examples and structured explanations, this text serves as a valuable resource for mastering deep learning architectures and applications.</abstract>
  <subject>
    <topic>Deep learning</topic>
  </subject>
  <subject>
    <topic>Machine learning</topic>
  </subject>
  <subject>
    <topic>Neural networks</topic>
  </subject>
  <classification authority="udc">004.85 RET</classification>
  <identifier type="isbn">9789347227417</identifier>
  <recordInfo>
    <recordChangeDate encoding="iso8601">20260902130101.0</recordChangeDate>
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