This repo contains my TA solutions for the graduate-level course 11-785: Introduction to Deep Learning. This is one of the most intense courses at CMU, and I was very fortunate to be a teaching assistant for it after taking the course. The course is currently being taught to over 350+ students.
The course covers everything from foundational concepts to advanced topics in Deep Learning, including neural networks, optimizations, and architectures like transformers. In the first parts of each homework, students implement various neural networks from scratch using only numpy, such as FFN, RNN, and GRU. In the second parts, they use PyTorch and learn to hyperparameter tune to solve tasks like language modeling and automatic speech recognition (ASR).
For course materials, check out the official site: https://deeplearning.cs.cmu.edu/F24/index.html