Image Classification Using Neural Networks

Introduction

This project is a practical demonstration of image classification using neural networks. Image classification plays a crucial role in computer vision, and this project specifically focuses on classifying handwritten digits from the MNIST dataset, a well-known dataset in machine learning. The code is implemented in Python using the Keras library, built on top of TensorFlow.

Table of Contents

1. Project Structure
2. Dependencies
3. Usage
4. Methodology
5. Results
6. License
7. Contact

Project Structure

The project directory is organized as follows:

• image_classification.ipynb: A Jupyter Notebook containing the code and explanations for the image classification project.
• README.md: This README file that provides an overview of the project and instructions on how to run the code.

Dependencies

To run the code in the Jupyter Notebook, you'll need the following dependencies:

• Python 3.x
• TensorFlow (2.x recommended)
• Keras (usually included with TensorFlow)
• NumPy
• Matplotlib
• Opencv

You can install the required dependencies using pip:

pip install tensorflow matplotlib numpy