Deep learning with PyTorch

Monday, November 16
9 am–5 pm Pacific Time

The format for this course will be a combination of interactive Zoom sessions, reading material, and videos.
Course materials will be added here shortly before the start of the school.

Instructor: Marie-Hélène Burle (WestGrid)

Prerequisites: Working knowledge of Python or attendance at the Basics of Python course.

Software: All attendees will need a remote secure shell (SSH) client installed on their computer. On Windows we recommend the free Home Edition of MobaXterm. On Mac and Linux computers SSH is usually pre-installed (try typing ssh in a terminal to make sure it is there).

Zoom   9–9:30 am Pacific Time
Introduction to the course & cluster access

On your own
AI, machine learning, deep learning
Which framework to choose?
(Optional) Local installation
Documentation
Artificial neural networks
How do neural networks learn?

Zoom   Noon–1:30 pm Pacific Time
Questions so far
PyTorch tensors
The MNIST

On your own
Backpropagation
A bit of calculus
Automatic differentiation

Zoom   3–5 pm Pacific Time
Our NN to classify the MNIST
Final questions

(Additional readings)
HPC with Python
A few notes on PyTorch distributed computing