This short course provides a practical introduction to building artificial neural networks using PyTorch, a powerful and flexible deep learning framework. The course covers the fundamentals of PyTorch, including tensors, automatic differentiation, and model building. Participants will learn how to construct, train, and optimize neural networks while exploring real-world applications. The course also includes best practices for experiment tracking and project setup. By the end of the course, participants will have a solid foundation in PyTorch and be able to develop deep learning models for a variety of tasks.

Attendees should be familiar with the materials covered in the Introduction to HPC workshop and have familiarity with Python.