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 focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.
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 workshop will go into the different ways python packages can be managed in a cluster environment using conda and python virtual environments both in batch mode from the command line and with Jupyter Notebooks and Jupyter Lab on the cluster. The examples will be run on the GMU HOPPER Cluster.
This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.
Fastai offers many tools to people working with machine learning and artifical intelligence including tutorials on PyTorch in addition to their own library built on PyTorch, news articles, and other resources to dive into this realm.
HPCwire is a prominent news and information source for the HPC community. Their website offers articles, analysis, and reports on HPC technologies, applications, and industry trends.
Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.
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.