These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.
This tutorial is essentially the "hello world" of image recognition and feed-forward neural network (using PyTorch). Using the MNIST database (filled within images of handwritten digits), the tutorial will instruct how to build a feed-forward neural network that can recognize handwritten digits. A solid understanding of feed-forward and back-propagation is recommended.