Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1504
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJulian, David-
dc.date.accessioned2026-07-28T06:47:18Z-
dc.date.available2026-07-28T06:47:18Z-
dc.date.issued2018-12-
dc.identifier.isbn978-1-78953-409-2-
dc.identifier.urihttp://hdl.handle.net/123456789/1504-
dc.description.abstractPyTorch is surprisingly easy to learn and provides advanced features such as a supporting multiprocessor, as well as distributed and parallel computation. PyTorch has a library of pre-trained models, providing out-of-the-box solutions for image classification. PyTorch offers one of the most accessible entry points into cutting-edge deep learning. It is tightly integrated with the Python programming language, so for Python programmers, coding it seems natural and intuitive. The unique, dynamic way of treating computational graphs means that PyTorch is both efficient and flexible.en_US
dc.language.isoenen_US
dc.publisherPackt Publishingen_US
dc.relation.ispartofseries1201218;-
dc.subjectDeep Learning with PyTorch Quick Start Guideen_US
dc.titleDeep Learning with PyTorch Quick Start Guideen_US
dc.title.alternativeLearn to train and deploy neural network models in Pythonen_US
dc.typeBooken_US
Appears in Collections:E-Books

Files in This Item:
File Description SizeFormat 
Deep Learning with PyTorch Quick Start.pdf6.53 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.