LEADER 02250nam 2200577 a 450 001 9910782372103321 005 20231206223443.0 010 $a1-299-56273-6 010 $a0-8070-3283-2 035 $a(CKB)1000000000541965 035 $a(EBL)3117992 035 $a(SSID)ssj0000117180 035 $a(PQKBManifestationID)11128846 035 $a(PQKBTitleCode)TC0000117180 035 $a(PQKBWorkID)10043264 035 $a(PQKB)11776864 035 $a(Au-PeEL)EBL6069404 035 $a(OCoLC)1058441161 035 $a(MiAaPQ)EBC3117992 035 $a(MiAaPQ)EBC6069404 035 $a(EXLCZ)991000000000541965 100 $a20070202d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCan we talk about race?$b[electronic resource] $eand other conversations in an era of school resegregation /$fBeverly Daniel Tatum 205 $a1st ed. 210 $aBoston, Mass. $cBeacon Press$d2007 215 $a1 online resource (169 p.) 225 0 $aRace, education, democracy series 300 $aDescription based upon print version of record. 311 $a0-8070-3285-9 311 $a0-8070-3284-0 320 $aIncludes bibliographical references (p. 135-147). 327 $a""Introduction: Can We Talk about Race?""; ""One: The Resegregation of Our Schools and the Affirmation of Identity""; ""Two: Connecting the Dots: How Race in Americaa???s Classrooms Affects Achievement""; ""Three: a???What Kind of Friendship Is That?a???: The Search for Authenticity, Mutuality, and Social Transformation in Cross-Racial Relationships""; ""Four: In Search of Wisdom: Higher Education for a Changing Democracy""; ""Afterword""; ""Acknowledgments""; ""Notes"" 410 0$aERACCE recommended resource. 606 $aSegregation in education$zUnited States 606 $aSchool integration$zUnited States 615 0$aSegregation in education 615 0$aSchool integration 676 $a379.2630973 700 $aTatum$b Beverly Daniel$01545554 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782372103321 996 $aCan we talk about race$93800538 997 $aUNINA LEADER 04359nam 22006255 450 001 9910157379403321 005 20200701140244.0 010 $a9781484222508 010 $a1484222504 024 7 $a10.1007/978-1-4842-2250-8 035 $a(CKB)3710000001000978 035 $a(DE-He213)978-1-4842-2250-8 035 $a(MiAaPQ)EBC4774158 035 $a(CaSebORM)9781484222508 035 $a(PPN)197459455 035 $a(OCoLC)1062727997 035 $a(OCoLC)on1062727997 035 $a(Perlego)3450095 035 $a(EXLCZ)993710000001000978 100 $a20161228d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMATLAB Machine Learning /$fby Michael Paluszek, Stephanie Thomas 205 $a1st ed. 2017. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017. 215 $a1 online resource (XIX, 326 p. 140 illus., 74 illus. in color.) 311 08$a9781484222492 311 08$a1484222490 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $a1 Overview of Machine Learning -- 2 The History of Machine Learning -- 3 Software for machine learning -- 4 Representation of data for Machine Learning in MATLAB -- 5 MATLAB Graphics -- 6 Machine Learning Examples in MATLAB -- 7 Face Recognition with Deep Learning -- 8 Data Classification -- 9 Classification of Numbers Using Neural Networks -- 10 Kalman Filters -- 11 Adaptive Control -- 12 Autonomous Driving -- Bibliography. 330 $aThis book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer?s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning. 606 $aArtificial intelligence 606 $aProgramming languages (Electronic computers) 606 $aComputer programming 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 615 0$aArtificial intelligence. 615 0$aProgramming languages (Electronic computers) 615 0$aComputer programming. 615 14$aArtificial Intelligence. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aProgramming Techniques. 676 $a006 700 $aPaluszek$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$0887778 702 $aThomas$b Stephanie$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910157379403321 996 $aMATLAB Machine Learning$91983078 997 $aUNINA