TUTORIALS
02/01/2024. 6-7:30 pm ET. Tutorial 1
Led by Yervand
Location: Northwest B101
We will be covering two topics mainly:
1. All the tools necessary for Assignment 1, and future assignments. This includes - git, conda, jupyter, PyTorch .
2. An introduction to using these above tools for Machine Learning.
If you're choosing "Track A" for assignment 1, please try to make it to this tutorial as it will help you solve bugs you might be running into!
Link to Tutorial 1 video and slides
02/08/2024. 6-7:30PM ET. Tutorial 2
Led by Morgan
Location: Northwest B101
This tutorial will include the following topics:1. Introduction to Dataset and Dataloader classes in Pytorch
2. Walkthrough of starter codebase for Assignment 2
3. Adversarial Examples
4. Introduction to some common types of neural networks (CNNs, RNNs, Autoencoders, GANs)
02/29/2024. 6-7:30PM ET Tutorial 3.
Led by Yervand
Location: Northwest B101
We will cover a number of topics, including:
1. transfer learning from pretrained models
2. image classification
3. generative models (e.g., autoencoders, Unet etc.)
The tutorial will be based entirely in Colab and is designed specifically to give beginners and intermediate practitioners some programmatic starting points to pursue a variety of projects.
03/07/2024. 6-7:30PM Tutorial 4
Led by Morgan
Location: Northwest B101
We will touch upon a number of topics, including:
1. Self-supervised learning
2. Natural language processing
3. Transformer architecture
4. Application of Transformer beyond language
The first half of the tutorial gives a tour on the concepts; and the second-half gives you hands-on experience with the models through a Colab notebook exercise.Link to Tutorial 4 video and slides
Link to Tutorial 4 Colab Notebook
