LEADER 01831oam 2200541 450 001 9910704694603321 005 20220222140417.0 035 $a(CKB)5470000002442765 035 $a(OCoLC)851076717$z(OCoLC)855049950 035 $a(EXLCZ)995470000002442765 100 $a20130627d2012 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aFederal child pornography offenses /$fUnited States Sentencing Commission 210 1$a[Washington, D.C.] :$cUnited States Sentencing Commission,$d[2012] 215 $a1 online resource (xiv, xxvi pages, 468 unnumbered pages) $ccolor illustrations 300 $a"December 2012"--USSC website. 300 $aTitle from title screen (viewed June 27, 2013). 320 $aIncludes bibliographical references. 606 $aChild pornography$xLaw and legislation$zUnited States 606 $aSentences (Criminal procedure)$zUnited States 606 $aPrison sentences$zUnited States 606 $aChild pornography$xLaw and legislation$2fast 606 $aPrison sentences$2fast 606 $aSentences (Criminal procedure)$2fast 607 $aUnited States$2fast 615 0$aChild pornography$xLaw and legislation 615 0$aSentences (Criminal procedure) 615 0$aPrison sentences 615 7$aChild pornography$xLaw and legislation. 615 7$aPrison sentences. 615 7$aSentences (Criminal procedure) 712 02$aUnited States Sentencing Commission, 801 0$bGPO 801 1$bGPO 801 2$bUCO 801 2$bCOO 801 2$bUOK 801 2$bGPO 801 2$bINT 801 2$bGPO 906 $aBOOK 912 $a9910704694603321 996 $aFederal child pornography offenses$93325738 997 $aUNINA LEADER 01121nam0 22003011i 450 001 UON00315160 005 20231205104114.334 010 $a88-424-9276-0 100 $a20080911d2005 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| 1|||| 200 1 $aˆLa ‰città biopolitica$emitologie della sicurezza$fAndrea Cavalletti 210 $aMilano$cB. Mondadori$dc2005 215 $a275 p.$d17 cm. 410 1$1001UON00067701$12001 $aTesti e pretesti 606 $aCittà$xPsicologia sociale$3UONC070005$2FI 606 $aSICUREZZA SOCIALE$xCentri urbani$3UONC070006$2FI 620 $aIT$dMilano$3UONL000005 676 $a307.76$cCittà$v21 700 1$aCavalletti$bAndrea$3UONV166800$0254750 712 $aBruno Mondadori$3UONV269427$4650 801 $aIT$bSOL$c20250606$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00315160 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI ITA Var 167 $eSI DC 927 5 167 996 $aCittá biopolitica$9229619 997 $aUNIOR LEADER 03869nam 22006255 450 001 9910349527903321 005 20230804140106.0 010 $a9781484249765 010 $a1484249763 024 7 $a10.1007/978-1-4842-4976-5 035 $a(CKB)4100000009382513 035 $a(DE-He213)978-1-4842-4976-5 035 $a(MiAaPQ)EBC5909956 035 $a(CaSebORM)9781484249765 035 $a(PPN)248604988 035 $a(OCoLC)1127651156 035 $a(OCoLC)on1127651156 035 $a(EXLCZ)994100000009382513 100 $a20190928d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Applied Deep Learning $eConvolutional Neural Networks and Object Detection /$fby Umberto Michelucci 205 $a1st ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (XVIII, 285 p. 88 illus., 28 illus. in color.) 300 $aIncludes index. 311 08$a9781484249758 311 08$a1484249755 320 $aIncludes bibliographical references. 327 $aChapter 1: Introduction and Development Environment Setup -- Chapter 2: TensorFlow: advanced topics -- Chapter 3: Fundamentals of Convolutional Neural Networks -- Chapter 4: Advanced CNNs and Transfer Learning -- Chapter 5: Cost functions and style transfer -- Chapter 6: Object classification - an introduction -- Chapter 7: Object localization - an implementation in Python -- Chapter 8: Histology Tissue Classification. 330 $aDevelop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. You will: See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets. 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aOpen source software 606 $aArtificial Intelligence 606 $aPython 606 $aOpen Source 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 0$aOpen source software. 615 14$aArtificial Intelligence. 615 24$aPython. 615 24$aOpen Source. 676 $a006.3 700 $aMichelucci$b Umberto$4aut$4http://id.loc.gov/vocabulary/relators/aut$01059671 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349527903321 996 $aAdvanced Applied Deep Learning$92507536 997 $aUNINA