Created by Hong Xu for University of Utah's Machine Learning class Spring 2020. You can find the video of the lecture here.
The structure of the tutorial is as follows
- Powerpoint first covers
- Frameworks Overview: why pytorch? which gives a brief overview of the frameworks and their history.
- Pytorch: Installation and running which is a quick installation guide.
- We move to a Jupyter Notebook to explain and run some code
- Pytorch: building blocks
- Pytorch: optimizer, scheduler and data loader
- Pytorch: MNIST step-by-step example
- We return to the Powerpoint to explore some visualization tools and look/compare with some Tensorflow code
- Pytorch: Ideal project structure
- Other resources Visualization and more
- Tensorflow briefly: MNIST example