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This Jupyter Notebook implements a Convolutional Neural Network (CNN) for recognizing hand-drawn digits using the famous MNIST dataset. The model is designed to classify grayscale images of handwritten digits (0 through 9) with high accuracy.

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harshssn23/Hand-Digit-Recognition-

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Overview

This repository contains a Jupyter Notebook implementing a hand digit recognition model using the MNIST dataset. The MNIST dataset consists of 28x28 pixel grayscale images of handwritten digits (0 through 9) and is a common benchmark for testing image processing systems.

Prerequisites

  • Python (>=3.6)
  • Jupyter Notebook

Installation

  1. Clone the repository:
    git clone https://github.com/harshssn23/hand-digit-recognition.git
    cd hand-digit-recognition

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This Jupyter Notebook implements a Convolutional Neural Network (CNN) for recognizing hand-drawn digits using the famous MNIST dataset. The model is designed to classify grayscale images of handwritten digits (0 through 9) with high accuracy.

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