- Course website: https://cs231n.github.io/
- Assignment 1 (Spring 2018): https://cs231n.github.io/assignments2018/assignment1/
- Assignment 2 (Spring 2018): https://cs231n.github.io/assignments2018/assignment2/
- Assignment 3 (Spring 2018): https://cs231n.github.io/assignments2018/assignment3/
This repository contains my notes & solutions to the assignments.
Please note that I was not enrolled for the course and my solution was not submitted, checked or graded.
- I run the assignments using a Docker container.
- For problems that don't require TensorFlow or PyTorch I use a basic miniconda-based container (based on the
continuumio/miniconda3
Docker container), which is set up in a way similar to what I have described here.-
To run the container:
docker run -p 9999:8888 --name CS231n -v ~/github/my_CS231n/:/app/data cs231n
where
cs231n
is the name of my Docker image. -
To restart the container after it has shut down:
docker start -ia CS231n
where
CS231n
is the name of my Docker container.
-
- For problems that use PyTorch I either use this Dockerfile locally, or work on AWS without Docker (see below).
- To run the more computationally heavy stuff that uses TensorFlow or PyTorch, I use AWS spot instances initialized with Amazon's "Deep Learning AMI (Ubuntu)" image. Here is a description of my workflow (under the section "AWS Deep Learning AMI").