understanding how deep learning works

this repo contains code for running a variety of different experiments attempting to understand deep learning via empirical experiments

organization

each folder contains a readme with code documentation, as well as comments in the code
the vision_fit and vision_analyze folders detail a number of experiments on multilayer perceptrons and convolutional neural networks using various datasets including MNIST, CIFAR, and custom datasets
the sparse_coding folder contains code for running and analyzing sparse coding on different sets of images
the mog folder contain code examples for fitting synthetic datasets generated as mixtures of Gaussians
the poly_fit folder contains code for fitting simple 1D polynomials
the scripts folder contains scripts for launching jobs on a slurm cluster
the eda folder contains minimum working examples for simple setups with various pytorch and scikit-learn functions

the scripts folder contains sample slurm scripts for launching jobs on a cluster
most of the experiments are time-consuming and should be parallelized over many machines
to do so, ssh into one of the scf nodes (e.g. legolas) and run module load python
set the parameters you want to sweep as lists in one of the submit*.py files
then run this file and it will automatically launch slurm jobs for each set of parameters