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.

What you'll learn

Training a network
Making predictions
Working with classes and tensors
Working with loss, autograd, and optimizers
Troubleshooting a PyTorch network
CPU/GPU usage

Title: Deep Learning with PyTorch Essential Training
Publisher: Linkedin.Learning
Size: 129M (135214561 B)
Files: 3F
Date: 10/03/2019
Course #: Linkedin.Learning
Type: N/A
Published: October 3, 2019
Modified: N/A
URL: www.linkedin.com/learning/deep-learning-with-pytorch-essential-training
Author: Jonathan Fernandes
Duration:
Skill: Intermediate
Exer/Code:

Installation:
Unpack that shit, run that shit

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