Digit Recognizer Neural Network

A deep learning project for handwritten digit recognition using Convolutional Neural Networks (CNNs) implemented with TensorFlow. The model is trained and evaluated on the MNIST dataset, achieving an accuracy of [your_accuracy_percentage]% on the test set.

Table of Contents

• Overview
• Features
• Usage
• Results
• Contributing
• License

Overview

This repository houses the source code for a neural network designed to recognize handwritten digits. The model is based on Convolutional Neural Networks, a powerful architecture for image classification tasks. The project uses the MNIST dataset, consisting of 28x28 pixel grayscale images of digits from 0 to 9.

Features

• Convolutional Neural Network (CNN): The model architecture is structured with CNNs for effective feature extraction in handwritten digit images.

• MNIST Dataset: The model is trained and evaluated on the MNIST dataset, providing a standardized benchmark for digit recognition models.

• TensorFlow Implementation: The code is implemented using TensorFlow, a widely-used deep learning framework. It is well-documented and organized for clarity and ease of use.

Usage

1. Clone the repository:

   git clone https://github.com/your-username/digit-recognizer-neural-network.git

Install Dependencies:

pip install -r requirements.txt

Run Training:

python train.py

Evaluate the Model:

python evaluate.py

Results

The model achieves an accuracy of 0.9776999950408936 on the test set. Detailed evaluation metrics and visualizations can be found in the results folder.