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  <titleInfo>
    <title>Deep learning algorithms</title>
  </titleInfo>
  <name type="personal">
    <namePart>Gacovski, Zoran</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
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    <dateIssued encoding="marc">2022</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>xxii,384 pages :  illustrations ;  24 cm.</extent>
  </physicalDescription>
  <tableOfContents>Section 1: Methods and approaches for deep learning -- Advancements in deep learning theory and applications: perspective in 2020 and beyond -- Deep ensemble reinforcement learning with multiple deep deterministic policy gradient algorithm -- Dynamic decision-making for stabilized deep learning software platforms -- Deep learning for hyperspectral data classification through exponential momentum deep convolution neural networks -- Ensemble network architecture for deep reinforcement learning -- Section 2: Deep learning techniques applied in biology -- Fish detection using deep learning -- Can deep learning identify tomato leaf disease? -- Deep learning for plant identification in natural environment -- Applying deep learning models to mouse behavior recognition -- Section 3: Deep learning applications in medicine -- Application of deep learning in neuroradiology: brain hemorrhage classification using transfer learning -- A review of the application of deep learning in brachytherapy -- Exploring deep learning and transfer learning for colonic polyp classification -- Deep learning algorithm for brain-computer interface -- Section 4: Deep learning in pattern recognition tasks -- The application of deep learning in airport visibility forecast -- Hierarchical representations feature deep learning for face recognition -- Review of research on text sentiment analysis based on deep learning -- Classifying hand written digits with deep learning -- Bitcoin price prediction based on deep learning methods.</tableOfContents>
  <note type="statement of responsibility">edited by Zoran Gacovski. </note>
  <note>Includes bibliographical references and index. </note>
  <subject authority="lcsh">
    <topic>Apprentissage automatique</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Machine learning</topic>
  </subject>
  <classification authority="ddc">006.31 D360</classification>
  <identifier type="isbn">9781774691830</identifier>
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