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My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
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Go to fileI present my assignment solutions for both 2020 course offerings: Stanford University CS231n (CNNs for Visual Recognition) and University of Michigan EECS 498-007/598-005 (Deep Learning for Computer Vision).
The similarity between the two courses is related to the fact that one of CS231n's main instructors (precisely, Justin Johnson) moved from Stanford to Michigan in 2019.
Assignments are the funniest part of the courses, they allow practicing most of the learned theoretical concepts. That is, you will implement vectorized mathematical formulas, gradient descent (be prepared to spend some hours with a pen and a sheet figuring out how to compute formula gradients), neural networks (among others: CNNs and RNNs) from scratch, etc. . That being said, in advanced assignment parts, you will also use high-level frameworks: TensorFlow and PyTorch.
Assignment questions are in form of Jupyter notebooks that call external Python files in order to execute properly. That is, you will mostly implement missing parts in the Python files and execute notebook's cells to check the correctness of your implementation. However, you'll write also some code in the notebooks and respond to inline questions (result analysis and theoretical questions).
For my implementation, I solved all from the three CS231n assignments, for the questions that use frameworks, they ask to pick only one, and for that I choosed PyTorch. That is, questions that require framework were implemented with PyTorch (and not with TensorFlow). For EECS 498-007, since its assignments are similar to the CS231n ones, I solved only those who bring new concepts, precisely A4 (partially, the first two questions about Residual Networks and Attention LSTM), A5 (Object detection: YOLO and Faster RCNN) and A6 (partially, the 1st question about VAEs). For EECS 498-007, there is no choice, only PyTorch is used (which fits perfectly with my choice of using it also in CS231n).
Note that, even that my coding solutions are probably correct, the CS231n assignments contain inline questions for which I'm not sure about their correctness, I just responded as well as I know. Also, Except for the CS231n first assignment (which is less commented), for the remaining assignments, I tried to comment on my code as richly as I can to make it understandable.
The repository file's structure is quite intuitive, there are two folders (one for each course), each one with its sub-folders that represent the assignments (three for both, CS231n and EECS 498-007). Note that for each assignment's folder, I put a README which shows covered topics and question descriptions (copied from the assignment's website).
The table below shows relevant links to both courses' materials.
| Relevent info | CS231n | EECS 498-007 |
|---|---|---|
| Official website | [2020], [2017] | [2020], [2019] |
| Lectures playlist | [2017], [2016] | [2019] |
| Syllabus | [2020], [2017] | [2020], [2019] |