<?xml version="1.0" encoding="utf-8" ?> <rss version="2.0" xmlns:opensearch="http://a9.com/-/spec/opensearch/1.1/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"> <channel> <title> <![CDATA[NUST Library Search for 'su:&quot;Deep learning Machine learning.&quot;']]> </title> <!-- prettier-ignore-start --> <link> /cgi-bin/koha/opac-search.pl?q=ccl=su%3A%22Deep%20learning%20Machine%20learning.%22&#38;sort_by=relevance&#38;format=rss </link> <!-- prettier-ignore-end --> <atom:link rel="self" type="application/rss+xml" href="/cgi-bin/koha/opac-search.pl?q=ccl=su%3A%22Deep%20learning%20Machine%20learning.%22&#38;sort_by=relevance&#38;format=rss" /> <description> <![CDATA[ Search results for 'su:&quot;Deep learning Machine learning.&quot;' at NUST Library]]> </description> <opensearch:totalResults>2</opensearch:totalResults> <opensearch:startIndex>0</opensearch:startIndex> <opensearch:itemsPerPage>50</opensearch:itemsPerPage> <atom:link rel="search" type="application/opensearchdescription+xml" href="/cgi-bin/koha/opac-search.pl?q=ccl=su%3A%22Deep%20learning%20Machine%20learning.%22&#38;sort_by=relevance&#38;format=opensearchdescription" /> <opensearch:Query role="request" searchTerms="q%3Dccl%3Dsu%253A%2522Deep%2520learning%2520Machine%2520learning.%2522" startPage="" /> <item> <title> Hyperparameter tuning for machine and deep learning with : a practical guide / </title> <dc:identifier>ISBN:9789811951695 (hardcover)</dc:identifier> <!-- prettier-ignore-start --> <link>/cgi-bin/koha/opac-detail.pl?biblionumber=171411</link> <!-- prettier-ignore-end --> <description> <![CDATA[ <p> Singapore : Springer, 2023 .<br /> xvii, 323 pages : 24 cm..<br /> 9789811951695 (hardcover) </p> ]]> <![CDATA[ <p> <a href="/cgi-bin/koha/opac-reserve.pl?biblionumber=171411">Place hold on <em>Hyperparameter tuning for machine and deep learning with :</em></a> </p> ]]> </description> <guid>/cgi-bin/koha/opac-detail.pl?biblionumber=171411</guid> </item> <item> <title> Use of deep learning algorithms towards dose optimization in non-contrast paediatric head CT examination : a phantom study / </title> <dc:identifier>ISBN:</dc:identifier> <!-- prettier-ignore-start --> <link>/cgi-bin/koha/opac-detail.pl?biblionumber=173157</link> <!-- prettier-ignore-end --> <description> <![CDATA[ <p> By Mutasa, Tatenda.<br /> Bulawayo, Zimbabwe : National University of Science and Technology, 2023 .<br /> xii, 65 pages : , A dissertation submitted in partial fulfillment of the requirements for the Bachelor of Science Honours degree in Radiography. 30 cm..<br /> </p> ]]> <![CDATA[ <p> <a href="/cgi-bin/koha/opac-reserve.pl?biblionumber=173157">Place hold on <em>Use of deep learning algorithms towards dose optimization in non-contrast paediatric head CT examination : </em></a> </p> ]]> </description> <guid>/cgi-bin/koha/opac-detail.pl?biblionumber=173157</guid> </item> </channel> </rss>
