Neural Networks: Basic Concepts

This repository is for a project that involves the construction of a basic neural network (MLP), and the performance comparison against the commonly used libraries (Py Torch and Tensorflow Keras).

If you aren't familiar with neural networks, go to the 'Artificial_Neural_Networks' file, where I explain the basics concepts with a MLP. If you're not familiar with Machine Learning either, you should learn it first in order to understand how a Deep learning model works.

Quick Start

In order to run all the codes succesfully, you'll need to create a python virtual environment and install all the necessary requirements.

Configure the Python Environment

1. Create the virtual environment (there is no mandatory version, but python 3.9 is recommended)

   python -m venv <venv_name>

2. Activate the environment

   source <venv_name>/bin/activate

   Whenever you want to get out from the environment, just run the next command:

   deactivate

Install the required python libraries and packages

1. Install the necessary libraries through the 'Makefile'

   pip install -r requirements.txt

Navigate through the project

Once you are all set, feel free to browse through the project.

• Go to 'src' to see the MLP class and its functions
• Go to 'test' to see the functions used for the unit tests, and try to test the code yourself

  pytest tests/test_neural_network.py

• Go to the Jupyter Notebooks which contain the examples, and run them yourself

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Notes

• Keep in mind that this project is very basic because the purpose is purely educational, the ANN created won't work properly in a project with production standards
• You can try with your own data sets (just be careful to do the right preprocessing), and with other configurations and topologies