Basics of Neural Networks and Algorithm Training
[This is session 4 in the series Introduction to Machine Learning in Python]
Overview:
One of the most discussed and perhaps mysterious machine learning models is the neural network. Neural networks are a kind of machine learning model inspired by biological processes taking place in the brain. This lesson will demystify neural networks and provide you with a plain-English explanation of how they work. We will train a neural network to recognize handwritten digits; this is a classification task. We will discuss some variants on neural networks such as convolutional neural networks. We will also discuss deep learning and further explore the training step in the machine learning pipeline.
By the end of the workshop, participants will be able to:
- Given a scaffolded environment and curated data set, follow a tutorial that trains a neural network to perform classification;
- Describe in plain English what a neural network is and what deep learning is;
- Describe at a high-level what the training process is for neural networks and distinguish it from the training processes seen previously.
Prerequisites: Participants should already have some familiarity with Python programming fundamentals, e.g. loops, conditional execution, importing modules, and calling functions. Furthermore, participants should ideally have attended the first lesson in the “Fundamentals of Machine Learning in Python” series, or they should already have some background on the general machine learning pipeline.
Date: Friday, 24 March 2023.
Time: 10 a.m. to 12 p.m.
Location: hybrid (in-person at Burnside Hall 1104, and online via Zoom).
Instructors: Jacob Errington, Faculty Lecturer, and Eric Mayhew, graduate student, School of Computer Science, McGill University.