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[null,"1.","Record Nr.",null,"UNINA9910704694603321"]
[null,null,"Titolo",null,"Federal child pornography offenses / / United States Sentencing Commission"]
[null,null,"Pubbl/distr/stampa",null,"[Washington, D.C.] : , : United States Sentencing Commission, , [2012]"]
[null,null,"Descrizione fisica",null,"1 online resource (xiv, xxvi pages, 468 unnumbered pages) : color illustrations"]
[null,null,"Soggetti",null,"Child pornography - Law and legislation - United States"]
[{"$nulls":4},"Sentences (Criminal procedure) - United States"]
[{"$nulls":4},"Prison sentences - United States"]
[{"$nulls":4},"Child pornography - Law and legislation"]
[{"$nulls":4},"Prison sentences"]
[{"$nulls":4},"Sentences (Criminal procedure)"]
[{"$nulls":4},"United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"December 2012\"--USSC website."]
[{"$nulls":4},"Title from title screen (viewed June 27, 2013)."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,"2.","Record Nr.",null,"UNINA9910349527903321"]
[null,null,"Autore",null,"Michelucci Umberto"]
[null,null,"Titolo",null,"Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection / / by Umberto Michelucci"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019"]
[null,null,"ISBN",null,"9781484249765"]
[{"$nulls":4},"1484249763"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVIII, 285 p. 88 illus., 28 illus. in color.)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Python (Computer program language)"]
[{"$nulls":4},"Open source software"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Python"]
[{"$nulls":4},"Open Source"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Chapter 1: Introduction and Development Environment Setup -- Chapter 2: TensorFlow: advanced topics -- Chapter 3: Fundamentals of Convolutional Neural Networks -- Chapter 4: Advanced CNNs and Transfer Learning -- Chapter 5: Cost functions and style transfer -- Chapter 6: Object classification - an introduction -- Chapter 7: Object localization - an implementation in Python -- Chapter 8: Histology Tissue Classification."]
[null,null,null,"Sommario/riassunto","Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO "]
[{"$nulls":4},"(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."]