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[null,"1.","Record Nr.",null,"UNINA990003942210403321"]
[null,null,"Autore",null,"Calamandrei, Daniele"]
[null,null,"Titolo",null,"Learning network : organizzare la conoscenza nei sistemi d'impresa / Daniele Calamandrei, Marco Migliari"]
[null,null,"Pubbl/distr/stampa",null,"Milano : FrancoAngeli, 2002"]
[null,null,"ISBN",null,"88-464-4169-9"]
[null,null,"Descrizione fisica",null,"103 p. ; 23 cm"]
[null,null,"Collana",null,"IRPET - Istituto regionale per la programmazione economica della Toscana ; 48"]
[null,null,"Disciplina",null,"658.3124"]
[null,null,"Locazione",null,"MAS"]
[null,null,"Collocazione",null,"VII-A-167"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910483187903321"]
[null,null,"Autore",null,"Gerard Charley"]
[null,null,"Titolo",null,"Practical machine learning in Javascript : tensorflow.js for web developers / / Charlie Gerard"]
[null,null,"Pubbl/distr/stampa",null,"[Place of publication not identified] : , : Apress, , [2021]"]
[{"$nulls":4},"Â©2021"]
[null,null,"ISBN",null,"1-5231-5067-X"]
[{"$nulls":4},"1-4842-6418-5"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVI, 323 p. 110 illus.)"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"TensorFlow"]
[{"$nulls":4},"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 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."]
[null,null,null,"Sommario/riassunto","Build 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 "]
[{"$nulls":4},"Develop dynamic and intelligent web content."]