https://github.com/csinva/dnn-experiments
A set of scripts and experiments making it easier to analyze deep learning empirically.
Science Score: 13.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○Scientific vocabulary similarity
Low similarity (9.8%) to scientific vocabulary
Keywords
ai
artificial-intelligence
cifar
computer-vision
deep-learning
machine-learning
ml
mnist
neural-network
polynomial
python
pytorch
sparse-coding
statistics
theory
Last synced: 5 months ago
·
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Repository
A set of scripts and experiments making it easier to analyze deep learning empirically.
Basic Info
Statistics
- Stars: 2
- Watchers: 4
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
ai
artificial-intelligence
cifar
computer-vision
deep-learning
machine-learning
ml
mnist
neural-network
polynomial
python
pytorch
sparse-coding
statistics
theory
Created over 7 years ago
· Last pushed over 5 years ago
Metadata Files
Readme
readme.md
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 visionfit and visionanalyze 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
requirements
- the code is all tested in python3 and pytorch 1.0
running
- the
scriptsfolder 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
Owner
- Name: Chandan Singh
- Login: csinva
- Kind: user
- Location: Microsoft research
- Company: Senior researcher
- Website: csinva.io
- Twitter: csinva_
- Repositories: 29
- Profile: https://github.com/csinva
Senior researcher @Microsoft interpreting ML models in science and medicine. PhD from UC Berkeley.
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