Resources: Reinforcement Learning

Exercises: Reinforcement Learning

Resources: Capstone Project

Exercises: Capstone Project

Resources: Transformers and Mechanistic Interpretability

Exercises: Transformers and Mechanistic Interpretability

Resources: Fundamentals

Exercises: Fundamentals

Resources: Prerequisites

Exercises: Prerequisites

The material on this page covers the first five days of the curriculum. It can be seen as a grounding in all the fundamentals necessary to complete the more advanced sections of this course (such as RL, transformers, mechanistic interpretability, and generative models).

Each exercise will have a difficulty and importance rating out of 5, as well as an estimated maximum time you should spend on these exercises and sometimes a short annotation. You should interpret the ratings & time estimates relatively (e.g. if you find yourself spending about 50% longer on the exercises than the time estimates, adjust accordingly). Please do skip exercises / look at solutions if you don't feel like they're important enough to be worth doing, and you'd rather get to the good stuff!

All of the material needed to complete these exercises, including readings, instructions, and hints, are available on this section's streamlit page. Please follow along with the streamlit page to complete the exercises, then submit your work here. 

By the end of the unit, you should be able to:
This week is focused on teaching you to:
\- Build your own 1D and 2D convolution functions
\- Build and load weights into a Residual Neural Network, and finetune it on a classification task
\- Work with weights and biases to optimize hyperparameters
\- Implement your own backpropogation methods


Submit a link to your exercises, through a GitHub repo, or your Collab notebooks, or through any other form you'd like. 


Ray Tracing Excercises

CNNs and Resnets Excercises

Submit a link to your exercises, through a GitHub repo, or your Collab notebooks, or through any other form you'd like. 



Optimization and Hyperparameters Exercises

 Submit a link to your exercises, through a GitHub repo, or your Collab notebooks, or through any other form you'd like. 


ARENA

Exercises: Fundamentals

Exercises