PyTorch Pocket Reference

PyTorch Pocket Reference
-0 %
Der Artikel wird am Ende des Bestellprozesses zum Download zur Verfügung gestellt.
 EPUB
Sofort lieferbar | Lieferzeit: Sofort lieferbar

Unser bisheriger Preis:ORGPRICE: 26,16 €

Jetzt 26,15 €* EPUB

Artikel-Nr:
9781492089957
Veröffentl:
2021
Einband:
EPUB
Seiten:
310
Autor:
Joe Papa
eBook Typ:
EPUB
eBook Format:
EPUB
Kopierschutz:
Adobe DRM [Hard-DRM]
Sprache:
Englisch
Beschreibung:

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 patternsCreate custom models and data transformsTrain and deploy models using a GPU and TPUTrain and test a deep learning classifierAccelerate training using optimization and distributed trainingAccess useful PyTorch libraries and the PyTorch ecosystem
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 patternsCreate custom models and data transformsTrain and deploy models using a GPU and TPUTrain and test a deep learning classifierAccelerate training using optimization and distributed trainingAccess useful PyTorch libraries and the PyTorch ecosystem

Kunden Rezensionen

Zu diesem Artikel ist noch keine Rezension vorhanden.
Helfen sie anderen Besuchern und verfassen Sie selbst eine Rezension.