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 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 Cluster account. If you do not already have
an account, you can visit our
getting started 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
Resources
For a self-guided version, you can read the Deep Learning in Pytorch for Beginners guide on our workshop site.