• Landing Page
• Pytorch Quickstart
• Pytorch Basics
• Pytorch GPU Support
• Regression and Classification with Fully Connected Neural Networks
• High Dimensional Data
• Datasets and data loading
• Building the network
• Computer Vision and Convolutional Neural Networks

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.

• Workshop Resources

4/5/25 - 4/6/25 The Duke IEEE Student Chapter is working with ACCESS to host a workshop on a introduction to supercomputing. All workshop resources are available on https://workshop.dukeieee.org/ Topics include: Here's a summarized list of topics for the event: Day 1: Saturday, April 5th Opening Remarks & ACCESS Overview (Including how to request compute usage with Jetstream 2) Tutorial 1: Introduction to Supercomputing Architecture, Linux, and Job Scheduling (SLURM) Tutorial 2: Containerized Large Language Model Inference and Finetuning Tutorial 3: Portable Code - Local Containers to HPC Scale Tutorial 4: ACCESS Pegasus - Serverless Data Processing Workflow in Jupyter Notebooks Networking & Hors D'oeuvres Day 2: Sunday, April 6th Tutorial 2: Deep Dive in AI Agents - "Building Superintelligence in 90 Minutes" by Harry Fazzone Tutorial 3: DASK - Python-based Distributed Computing Framework for HPC Tutorial 4: Basic Parallelism & MPI by Rebecca Hartman-Baker, PhD (NERSC) Closing Talk: Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part One, 1982)