Digit Recognition Project

Overview

This project focuses on digit recognition using TensorFlow and offers two different approaches for achieving high accuracy:

• Using Dense Layers: Model is trained using dense layers, achieving a training accuracy of 98% and a test accuracy of 97%.

• Using CNN Layers: Model is trained using Convolutional Neural Network (CNN) layers, achieving an impressive training accuracy of 99.5% and a test accuracy of 98.5%.

It aims to classify handwritten digits from the MNIST dataset.

Project Structure

The project is organized into two main folders:

• Using CNN: Contains code related to digit recognition using Convolutional Neural Networks.
• Using Dense: Contains code related to digit recognition using Dense Neural Networks.

Getting Started

Follow the steps below to get started with this project:

1. Clone this repository by running git clone https://github.com/yourusername/digit-recognition.git

2. Navigate to the appropriate folder (CNN or Dense) to explore the specific code.

Dependencies

• Python 3
• Keras
• TensorFlow
• NumPy
• Matplotlib