PyTorch Essential Training: Deep Learning.
(Online Course)
Author
Contributors
Published
Carpenteria, CA linkedin.com, 2019.
Format
Online Course
Status
Description
Loading Description...
Also in this Series
Checking series information...
More Details
Language
English
Notes
General Note
10/03/201912:00:00AM
Participants/Performers
Presenter: Jonathan Fernandes
Description
Explore the basics of deep learning using PyTorch. Learn about the components of an image recognition model using the Fashion MNIST dataset.
Description
PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.
System Details
Latest version of the following browsers: Chrome, Safari, Firefox, or Internet Explorer. Adobe Flash Player Plugin. JavaScript and cookies must be enabled. A broadband Internet connection.
Citations
APA Citation, 7th Edition (style guide)
Fernandes, J. (2019). PyTorch Essential Training: Deep Learning . linkedin.com.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Fernandes, Jonathan. 2019. PyTorch Essential Training: Deep Learning. linkedin.com.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Fernandes, Jonathan. PyTorch Essential Training: Deep Learning linkedin.com, 2019.
MLA Citation, 9th Edition (style guide)Fernandes, Jonathan. PyTorch Essential Training: Deep Learning linkedin.com, 2019.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.