AI Project

This project implements various neural network architectures using PyTorch for different tasks, including classification of the Iris dataset and handwritten digit recognition using the MNIST dataset.

Project Structure

AI-Project
├── .github
│   └── workflows
│       └── run-tests.yml
├── ANN
│   └── basic_nn.py
├── CNN
│   └── cnn.py
├── RNN
│   └── rnn.py
├── GRU
│   └── gru.py
├── LSTM
│   └── lstm.py
├── requirements.txt
└── README.md

Overview

• ANN (Artificial Neural Network): Implements a basic feedforward neural network to classify the Iris dataset.
• CNN (Convolutional Neural Network): Implements a convolutional neural network to classify handwritten digits from the MNIST dataset.
• RNN (Recurrent Neural Network): Implements a recurrent neural network for classifying handwritten digits from the MNIST dataset.
• GRU (Gate Recurrent Unit Neural Network): Implements a gate recurrent unit neural network for classifying handwritten digits from the MNIST dataset.
• LSTM (Long-Short Term Neural Network): Implements a long-short term neural network for classifying handwritten digits from the MNIST dataset.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/yourusername/AI-Project.git
   cd AI-Project
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Usage

• To train the ANN model on the Iris dataset, run:

  python ANN/basic_nn.py
  

• To train the CNN model on the MNIST dataset, run:

  python CNN/cnn.py
  

• To train the RNN model on the MNIST dataset, run:

  python RNN/rnn.py
  

• To train the GRU model on the MNIST dataset, run:

  python GRU/gru.py
  

• To train the LSTM model on the MNIST dataset, run:

  python LSTM/lstm.py
  

License

This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License License.