Deep Learning Project - 2023/24
This repository is a template repository for the homeworks to be developed in the Deep Learning course.
Deep Learning is a course of the
- Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua, Italy.
- Master Degree in Data Science of the Department of Mathematics "Tullio Levi-Civita", University of Padua, Italy.
| NAME | SURNAME | |
|---|---|---|
| Francesco | Chemello | francesco.chemello.1@studenti.unipd.it |
| Gabriella | Farias | gabriellaingridy.desouzafarias@studenti.unipd.it |
| Pietro | Volpato | pietro.volpato@studenti.unipd.it |
The repository is organised as follows:
- code: folder that contains all the code for the homework + files for git.
- presentation: this folder contains the final presentation of the course.
- slides: this folder contains the slides used for presenting the project.
- developing: contains the files that the group uses for developing the application.
- Retrieve data from .csv files (training, validation and test set).
- OneHot coding for alphabetic to numeric (4xN matrix, N = sting size).
- Reshape the PyTorch tensor to fit the data.
- Implementation of RNN (GRU NN because it is simpler and has better performance).
- Training with RNN epochs or K-fold for a better use of data (in this case we can implement it with Colab).
- Run on the test set.
- Performance evaluation: metrics used by ViraMiner.
- Discussion of results + ppt presentation of work or pdf (as directed by the professor).
Link to Google Colab file: https://colab.research.google.com/github/FrancescoChemello/Deep-Learning-Project/blob/main/code/project-colab.ipynb?authuser=1#scrollTo=Jbw00zSUP9u5&uniqifier=1
All the contents of this repository are shared using the Creative Commons Attribution-ShareAlike 4.0 International License.
