Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/1504| Title: | Deep Learning with PyTorch Quick Start Guide |
| Other Titles: | Learn to train and deploy neural network models in Python |
| Authors: | Julian, David |
| Keywords: | Deep Learning with PyTorch Quick Start Guide |
| Issue Date: | Dec-2018 |
| Publisher: | Packt Publishing |
| Series/Report no.: | 1201218; |
| Abstract: | PyTorch 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. |
| URI: | http://hdl.handle.net/123456789/1504 |
| ISBN: | 978-1-78953-409-2 |
| Appears in Collections: | E-Books |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Deep Learning with PyTorch Quick Start.pdf | 6.53 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
