Convolutional Neural Network (CNN) with MNIST Dataset This repository contains a Convolutional Neural Network (CNN) implementation for image classification using the MNIST dataset. The MNIST dataset is a popular dataset for handwritten digit recognition, making it a great starting point for learning about CNNs.
Prerequisites Before you begin, ensure you have met the following requirements:
Python: You should have Python installed (recommended version 3.6 or later). TensorFlow: You need to install TensorFlow, a popular deep learning library, which provides tools for working with neural networks. Jupyter Notebook (optional): You can use Jupyter Notebook for running and experimenting with the provided code. MNIST Dataset: The MNIST dataset is included in TensorFlow, so you don't need to download it separately. Getting Started To get started with this project, follow these steps:
Clone this repository to your local machine:
bash Copy code git clone https://github.com/your-username/cnn-mnist.git Change your working directory to the project folder:
bash Copy code cd cnn-mnist Install the required Python libraries:
bash Copy code pip install -r requirements.txt Open and run the Jupyter Notebook:
bash Copy code jupyter notebook cnn_mnist.ipynb Follow the instructions in the Jupyter Notebook to train and test the CNN model on the MNIST dataset.
Project Structure The project has the following structure:
cnn_mnist.ipynb: A Jupyter Notebook containing the code for the CNN implementation. requirements.txt: A list of Python libraries required for the project. Usage You can use this project as a learning resource to understand how to build and train a CNN for image classification. The provided Jupyter Notebook contains step-by-step explanations and code snippets to guide you through the process.
Contributing If you'd like to contribute to this project, please follow these steps:
Fork the repository. Create a new branch for your feature or bug fix. Make your changes and commit them. Push your changes to your fork. Create a pull request, describing your changes. License This project is licensed under the MIT License. See the LICENSE file for details.
Contact If you have any questions or need further assistance, feel free to contact AJIBOYE ABAYOMI at abayomiabayomi46111@gmail.com.
Happy learning and experimenting with CNNs and the MNIST dataset!