<?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[Air University Central Library Search for 'au:&quot;Raschka, Sebastian &quot;']]> </title> <!-- prettier-ignore-start --> <link> /cgi-bin/koha/opac-search.pl?q=ccl=au%3A%22Raschka%2C%20Sebastian%20%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=au%3A%22Raschka%2C%20Sebastian%20%22&#38;sort_by=relevance&#38;format=rss" /> <description> <![CDATA[ Search results for 'au:&quot;Raschka, Sebastian &quot;' at Air University Central Library]]> </description> <opensearch:totalResults>3</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=au%3A%22Raschka%2C%20Sebastian%20%22&#38;sort_by=relevance&#38;format=opensearchdescription" /> <opensearch:Query role="request" searchTerms="q%3Dccl%3Dau%253A%2522Raschka%252C%2520Sebastian%2520%2522" startPage="" /> <item> <title> Build a Large Language Model / </title> <dc:identifier>ISBN:9781633437166</dc:identifier> <!-- prettier-ignore-start --> <link>/cgi-bin/koha/opac-detail.pl?biblionumber=34934</link> <!-- prettier-ignore-end --> <description> <![CDATA[ <img src="https://images-na.ssl-images-amazon.com/images/P/1633437167.01.TZZZZZZZ.jpg" alt="" /> ]]> <![CDATA[ <p> By Raschka,Sebastian .<br /> [N.P]: Manning, 2025 .<br /> 368 p 20cm x 27cm.<br /> 9781633437166 </p> ]]> <![CDATA[ <p> <a href="/cgi-bin/koha/opac-reserve.pl?biblionumber=34934">Place hold on <em>Build a Large Language Model /</em></a> </p> ]]> </description> <guid>/cgi-bin/koha/opac-detail.pl?biblionumber=34934</guid> </item> <item> <title> Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 </title> <dc:identifier>ISBN:9781789955750</dc:identifier> <!-- prettier-ignore-start --> <link>/cgi-bin/koha/opac-detail.pl?biblionumber=35178</link> <!-- prettier-ignore-end --> <description> <![CDATA[ <img src="https://images-na.ssl-images-amazon.com/images/P/1789955750.01.TZZZZZZZ.jpg" alt="" /> ]]> <![CDATA[ <p> By Raschka, Sebastian.<br /> Birmingham Packet Publishing 2019 .<br /> xxi, 741p. , Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. 9781789955750 </p> ]]> <![CDATA[ <p> <a href="/cgi-bin/koha/opac-reserve.pl?biblionumber=35178">Place hold on <em>Python Machine Learning</em></a> </p> ]]> </description> <guid>/cgi-bin/koha/opac-detail.pl?biblionumber=35178</guid> </item> <item> <title> Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python </title> <dc:identifier>ISBN:978-1801819312 </dc:identifier> <!-- prettier-ignore-start --> <link>/cgi-bin/koha/opac-detail.pl?biblionumber=46999</link> <!-- prettier-ignore-end --> <description> <![CDATA[ <img src="https://images-na.ssl-images-amazon.com/images/P/1801819319.01.TZZZZZZZ.jpg" alt="" /> ]]> <![CDATA[ <p> By Raschka, Sebastian .<br /> UK : Packt : 2022 .<br /> xxix, 741 p : 978-1801819312 </p> ]]> <![CDATA[ <p> <a href="/cgi-bin/koha/opac-reserve.pl?biblionumber=46999">Place hold on <em>Machine Learning with PyTorch and Scikit-Learn:</em></a> </p> ]]> </description> <guid>/cgi-bin/koha/opac-detail.pl?biblionumber=46999</guid> </item> </channel> </rss>
