A project exploring neural networks and deep learning concepts. This repository contains code for training and evaluating a neural network on the MNIST dataset. Key features include:
- Data loading, preprocessing, and splitting (train, validation, test).
- Implementation of a feed-forward neural network using PyTorch.
- Model training using the Rprop optimization algorithm.
- Hyperparameter tuning using K-fold cross-validation and random search.
- Evaluation metrics including accuracy, loss, confusion matrix, and classification report.
- Early stopping mechanism to prevent overfitting.
- Visualization of training progress and results.
- Linux operating system
- An Nvidia GPU compatible with the latest CUDA version
- Update system packages:
sudo apt update && sudo apt upgrade
- Install build-essential and CUDA dependencies:
sudo apt install build-essential nvidia-cuda-toolkit- If you are using WSL or other platforms install the CUDA dependencies from the official website
- Create a new python virtual environment in the project direcory:
python -m venv .venv
- Activate the virtual environment:
source .venv/bin/activate
- Install the required packages:
pip install -r requirements.txt, this will install the required packages