{"id":15906913,"url":"https://github.com/csinva/dnn-experiments","last_synced_at":"2025-04-02T23:26:49.531Z","repository":{"id":84627306,"uuid":"143078863","full_name":"csinva/dnn-experiments","owner":"csinva","description":"A set of scripts and experiments making it easier to analyze deep learning empirically.","archived":false,"fork":false,"pushed_at":"2020-06-13T22:39:43.000Z","size":44004,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-02-08T13:43:05.993Z","etag":null,"topics":["ai","artificial-intelligence","cifar","computer-vision","deep-learning","machine-learning","ml","mnist","neural-network","polynomial","python","pytorch","sparse-coding","statistics","theory"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","readme":"# understanding how deep learning works\nthis repo contains code for running a variety of different experiments attempting to understand deep learning via empirical experiments\n\n# organization\n- each folder contains a readme with code documentation, as well as comments in the code\n- 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\n- the sparse_coding folder contains code for running and analyzing sparse coding on different sets of images\n- the mog folder contain code examples for fitting synthetic datasets generated as mixtures of Gaussians\n- the poly_fit folder contains code for fitting simple 1D polynomials\n- the scripts folder contains scripts for launching jobs on a slurm cluster\n- the eda folder contains minimum working examples for simple setups with various pytorch and scikit-learn functions\n\n# requirements\n- the code is all tested in python3 and pytorch 1.0\n\n# running\n- the `scripts` folder contains sample slurm scripts for launching jobs on a cluster\n- most of the experiments are time-consuming and should be parallelized over many machines\n- to do so, ssh into one of the scf nodes (e.g. legolas) and run ```module load python```\n- set the parameters you want to sweep as lists in one of the submit*.py files\n- then run this file and it will automatically launch slurm jobs for each set of parameters\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Fdnn-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsinva%2Fdnn-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsinva%2Fdnn-experiments/lists"}