LEADER 01360nam2-2200373---450- 001 990003593860203316 005 20111122134405.0 035 $a000359386 035 $aUSA01000359386 035 $a(ALEPH)000359386USA01 035 $a000359386 100 $a20111121d2008----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aa---||||001yy 200 1 $a<> tecniche del disegno rinascimentale$edai materiali allo stile$eatti del convegno internazionale$eFirenze, Kunsthistorischen Institut, 22-23 settembre 2008$fa cura di Marzia Faietti, Lorenza Melli, Alessandro Nova 210 $aFirenze$cKunsthistorischen Institut$d2008 215 $a288 p.$cill.$d27 cm 461 0$10010104303$12001$aMitteilungen des kunsthistorischen Institutes in Florenz 606 0 $aDisegno$xTecnica$yItalia$zSec. 14.-16.$xAtti di congressi$2BNCF 676 $a741.20945 702 1$aFAIETTI,$bMarzia 702 1$aMELLI,$bLorenza 702 1$aNOVA,$bAlessandro 801 0$aIT$bsalbc$gISBD 912 $a990003593860203316 951 $aXII.2.B. 1641$b228718 L.M.$cXII.2.B.$d00268399 959 $aBK 969 $aUMA 979 $aIANNONE$b90$c20111121$lUSA01$h0941 979 $aIANNONE$b90$c20111121$lUSA01$h0946 979 $aIANNONE$b90$c20111122$lUSA01$h1344 996 $aTecniche del disegno rinascimentale$91136405 997 $aUNISA LEADER 02972oam 2200505 450 001 9910483187903321 005 20210506092556.0 010 $a1-5231-5067-X 010 $a1-4842-6418-5 024 7 $a10.1007/978-1-4842-6418-8 035 $a(CKB)4100000011586083 035 $a(DE-He213)978-1-4842-6418-8 035 $a(MiAaPQ)EBC6404857 035 $a(CaSebORM)9781484264188 035 $a(PPN)252511042 035 $a(EXLCZ)994100000011586083 100 $a20210506d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical machine learning in Javascript $etensorflow.js for web developers /$fCharlie Gerard 205 $a1st ed. 2021. 210 1$a[Place of publication not identified] :$cApress,$d[2021] 210 4$d©2021 215 $a1 online resource (XVI, 323 p. 110 illus.) 311 $a1-4842-6417-7 327 $aChapter 1: The Basics of Machine Learning -- Chapter 2: Tensorflow.js -- Chapter 3: Building an Image Classifier -- Chapter 4: -- Text Classification and Sentiment Analysis -- Chapter 5: Experimenting with Inputs -- Chapter 6: Machine Learning in Production -- Chapter 7: Bias in Machine Learning. 330 $aBuild machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You?ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your already honed skills as a web developer, you?ll add a whole new field of development to your skill set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. Get started in machine learning with web technologies. Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js?an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices. You will: Use the JavaScript framework for ML Build machine learning applications for the web Develop dynamic and intelligent web content. 606 $aMachine learning 606 $aTensorFlow 606 $aArtificial intelligence 615 0$aMachine learning. 615 0$aTensorFlow. 615 0$aArtificial intelligence. 676 $a006.31 700 $aGerard$b Charley$01226876 801 0$bCaPaEBR 801 1$bCaPaEBR 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483187903321 996 $aPractical machine learning in Javascript$92848759 997 $aUNINA