<?xml version="1.0" encoding="UTF-8"?>
<record
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd"
    xmlns="http://www.loc.gov/MARC21/slim">

  <leader>02972cam a22004217i 4500</leader>
  <controlfield tag="001">21795607</controlfield>
  <controlfield tag="003">EG-ScBUE</controlfield>
  <controlfield tag="005">20220302104116.0</controlfield>
  <controlfield tag="008">201109t2021    nyua   f b    001 0 eng d</controlfield>
  <datafield tag="020" ind1=" " ind2=" ">
    <subfield code="a">9781260462296</subfield>
  </datafield>
  <datafield tag="020" ind1=" " ind2=" ">
    <subfield code="a">1260462293</subfield>
  </datafield>
  <datafield tag="035" ind1=" " ind2=" ">
    <subfield code="a">(OCoLC)on1245422280</subfield>
  </datafield>
  <datafield tag="040" ind1=" " ind2=" ">
    <subfield code="a">NWQ</subfield>
    <subfield code="b">eng</subfield>
    <subfield code="e">rda</subfield>
    <subfield code="c">NWQ</subfield>
    <subfield code="d">OCLCO</subfield>
    <subfield code="d">YDXIT</subfield>
    <subfield code="d">OCLCF</subfield>
    <subfield code="d">DLC</subfield>
    <subfield code="d">EG-ScBUE</subfield>
  </datafield>
  <datafield tag="082" ind1="0" ind2="4">
    <subfield code="a">006.31</subfield>
    <subfield code="2">22</subfield>
    <subfield code="b">KON</subfield>
  </datafield>
  <datafield tag="100" ind1="1" ind2=" ">
    <subfield code="a">Konasani, Venkat Reddy,</subfield>
    <subfield code="e">author.</subfield>
  </datafield>
  <datafield tag="245" ind1="1" ind2="0">
    <subfield code="a">Machine learning and deep learning using Python and Tensorflow /</subfield>
    <subfield code="c">Venkat Reddy Konasani, Shailendra Kadre.</subfield>
  </datafield>
  <datafield tag="264" ind1=" " ind2="1">
    <subfield code="a">New York :</subfield>
    <subfield code="b">McGraw-Hill,</subfield>
    <subfield code="c">[2021]</subfield>
  </datafield>
  <datafield tag="264" ind1=" " ind2="4">
    <subfield code="c">&#xA9;2021</subfield>
  </datafield>
  <datafield tag="300" ind1=" " ind2=" ">
    <subfield code="a">xix, 533 pages :</subfield>
    <subfield code="b">illustrations ;</subfield>
    <subfield code="c">27 cm</subfield>
  </datafield>
  <datafield tag="336" ind1=" " ind2=" ">
    <subfield code="a">text</subfield>
    <subfield code="b">txt</subfield>
    <subfield code="2">rdacontent</subfield>
  </datafield>
  <datafield tag="337" ind1=" " ind2=" ">
    <subfield code="a">unmediated</subfield>
    <subfield code="b">n</subfield>
    <subfield code="2">rdamedia</subfield>
  </datafield>
  <datafield tag="338" ind1=" " ind2=" ">
    <subfield code="a">volume</subfield>
    <subfield code="b">nc</subfield>
    <subfield code="2">rdacarrier</subfield>
  </datafield>
  <datafield tag="504" ind1=" " ind2=" ">
    <subfield code="a">Includes bibliographical references and index.</subfield>
  </datafield>
  <datafield tag="505" ind1="0" ind2=" ">
    <subfield code="a">Introduction to machine learning and deep learning -- Basics of Python programming and statistics -- Regression and logistic regression -- Decision trees -- Model selection and cross-validation -- Cluster analysis -- Random forests and boosting -- Artificial neural networks -- TensorFlow and Keras -- Deep learning hyperparameters -- Convolutional neural networks -- Recurrent neural networks and long short-term memory.</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">"This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today's smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts; Python programming and statistics fundamentals; Regression and logistic regression; Decision trees; Model selection and cross-validation; Cluster analysis; Random forests and boosting; Artificial neural networks; TensorFlow and Keras; Deep learning hyperparameters; Convolutional neural networks; Recurrent neural networks and long short-term memory."--</subfield>
    <subfield code="c">Page 4 of cover.</subfield>
  </datafield>
  <datafield tag="630" ind1="0" ind2="7">
    <subfield code="a">TensorFlow.</subfield>
    <subfield code="2">BUEsh</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="7">
    <subfield code="a">Machine learning.</subfield>
    <subfield code="2">BUEsh</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="7">
    <subfield code="a">Neural networks (Computer science)</subfield>
    <subfield code="2">BUEsh</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="7">
    <subfield code="a">Python (Computer program language)</subfield>
    <subfield code="2">BUEsh</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="7">
    <subfield code="a">Artificial intelligence.</subfield>
    <subfield code="2">BUEsh</subfield>
    <subfield code="9">37100</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="b">COMSCI</subfield>
    <subfield code="c">February2022</subfield>
  </datafield>
  <datafield tag="655" ind1=" " ind2=" ">
    <subfield code="v">Reading book</subfield>
    <subfield code="9">34232</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
    <subfield code="a">Kadre, Shailendra,</subfield>
    <subfield code="e">author.</subfield>
  </datafield>
  <datafield tag="906" ind1=" " ind2=" ">
    <subfield code="a">7</subfield>
    <subfield code="b">cbc</subfield>
    <subfield code="c">copycat</subfield>
    <subfield code="d">2</subfield>
    <subfield code="e">ncip</subfield>
    <subfield code="f">20</subfield>
    <subfield code="g">y-gencatlg</subfield>
  </datafield>
  <datafield tag="942" ind1=" " ind2=" ">
    <subfield code="2">ddc</subfield>
    <subfield code="c">BB</subfield>
  </datafield>
  <datafield tag="999" ind1=" " ind2=" ">
    <subfield code="c">29813</subfield>
    <subfield code="d">29784</subfield>
  </datafield>
  <datafield tag="952" ind1=" " ind2=" ">
    <subfield code="0">0</subfield>
    <subfield code="1">0</subfield>
    <subfield code="2">ddc</subfield>
    <subfield code="4">0</subfield>
    <subfield code="7">0</subfield>
    <subfield code="8">Alahram</subfield>
    <subfield code="a">MAIN</subfield>
    <subfield code="b">MAIN</subfield>
    <subfield code="c">LOW</subfield>
    <subfield code="d">2022-03-02</subfield>
    <subfield code="e">Purchase</subfield>
    <subfield code="g">1699.00</subfield>
    <subfield code="h">281</subfield>
    <subfield code="l">1</subfield>
    <subfield code="m">10</subfield>
    <subfield code="o">006.31 KON</subfield>
    <subfield code="p">000056172</subfield>
    <subfield code="r">2025-07-15 00:00:00</subfield>
    <subfield code="s">2023-10-04</subfield>
    <subfield code="v">2123.75</subfield>
    <subfield code="w">2022-03-02</subfield>
    <subfield code="y">BB</subfield>
  </datafield>
</record>
