This project implements a neural network model using TensorFlow and Keras to recognize handwritten digits as either 0 or 1. The model is trained, evaluated, and tested using the MNIST dataset, demonstrating effective binary classification performance.
The goal of this project is to develop a machine learning model that can accurately distinguish between the handwritten digits '0' and '1'. The MNIST dataset is utilized for training and testing the model. The project explores the use of a simple neural network architecture to achieve high accuracy in binary digit recognition.
To run this project, you will need to have Python installed along with the following libraries:
- numpy
- tensorflow
- matplotlib
You can install the required libraries using pip:
pip install numpy tensorflow matplotlib- Clone the repository:
git clone https://github.com/HoomKh/Binary-Handwritten-Digit-Recognition.git
cd Binary-Handwritten-Digit-Recognition- Open the Jupyter Notebook:
jupyter notebook Handwritten-Digit-Recognition-Binary.ipynb- Run the cells in the notebook to train and test the model.
The model is evaluated on the test set to determine its accuracy. Visualizations of the training process and the model's predictions are included in the notebook.
Contributions are welcome! If you have any suggestions or improvements, please submit a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for details.
Happy coding! If you have any questions, feel free to reach out.