https://github.com/s3gmentati0nfaultuni/autoencoders

Autoencoders implementation for the 8th sheet of the course of Machine Learning and Physics at Heidelberg University
https://github.com/s3gmentati0nfaultuni/autoencoders

autoencoder machine-learning notebook-jupyter projects

Last synced: about 2 months ago
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Autoencoders implementation for the 8th sheet of the course of Machine Learning and Physics at Heidelberg University

• Host: GitHub
• URL: https://github.com/s3gmentati0nfaultuni/autoencoders
• Owner: S3gmentati0nFaultUni
• License: agpl-3.0
• Created: 2023-12-26T15:08:18.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-30T17:49:58.000Z (5 months ago)
• Last Synced: 2024-08-20T09:57:17.718Z (4 months ago)
• Topics: autoencoder, machine-learning, notebook-jupyter, projects
• Language: Jupyter Notebook
• Homepage:
• Size: 6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Autoencoders

## What about it

In this small project (bonus exercise sheet for the course of Machine Learning and Physics) the objective was to build an autoencoder using various two different sub-architectures, the first one being MLPs and the second one being CNNs. As a dataset we got a set of jet pictures from professot Tilman Plehn and we had to come up with a series of observations on how the two architectures that we came up with were able to reconstruct the images that were given as inputs.

## Work in progress

I still want to put some effort in this project, even though the due date is already over, because I want to understand a couple of things that didn't really sit right with me, here is an approximative roadmap of the process:

- [ ] Clean the code and make it easier to understand

- [x] Understand why the CNN has lower accuracy than the MLP substructure

    I found a way to make sure that the CNN has higher accuracy than the MLP substructure but it

    takes 5 times to run and the ROC curve is kind of a weird result.

- [ ] Make the Convolutional Neural Network more efficient.

## Quality of the solution

Even though our solution was not totally complete it was deemed close to perfect, thus it's possible for anyone to see it as a tutorial that is very close to beginner level (bragging, but still down to earth).