PyTorch Pocket Reference / Joe Papa

Public ISBD Titre : PyTorch Pocket Reference : Building and Deploying Deep Learning Models Type de document : texte imprimé Auteurs : Joe Papa, Auteur Editeur : Paris : O'Reilly Année de publication : 2021 Importance : 293 pages ISBN/ISSN/EAN : 978-1-4920-9000-7 Prix : 16645f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem PyTorch Pocket Reference : Building and Deploying Deep Learning Models [texte imprimé] / Joe Papa, Auteur . - Paris : O'Reilly, 2021 . - 293 pages. ISBN : 978-1-4920-9000-7 : 16645f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
100075446	K.45 PAP	Livre	Salle " Abraham Lincoln"	AL-E10a	A Consulter sur Place Exclu du prêt

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