Introduction to Deep Learning in PyTorch
This workshop series introduces students to the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading to training and evaluation. Throughout the sessions, students will participate in writing and executing simple deep learning programs using PyTorch – a popular Python library for developing, training, and deploying deep learning models.
Course Outline
- Conceptual Introduction to Deep Learning
- Using PyTorch on the Palmetto 2 cluster
- Introduction to the PyTorch Library
- Tensors, Parameters, Modules
- Computing on GPU
- Datasets and Data loading
- Custom models
- Training and evaluation
- Worked Deep Learning Example
Prerequisites
All students should have a Palmetto 2 cluster account. If you do not already
have an account, you can visit our
Getting Started With Palmetto 2 page.
Students should be familiar with the Python programming language. This
requirement could be fulfilled by personal projects, coursework or completion of
the
Introduction to Python Programming
workshop series. Experience in numeric computing with the numpy library is
helpful but not required.
Live Workshop
Session #1 for Spring 2026
Resources
For a self-guided version, you can read the Deep Learning in PyTorch for Beginners guide on our workshop site.