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A newer version of the Gradio SDK is available: 6.22.0

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metadata 
title: MNIST Digit Recognizer
emoji: 🔢
colorFrom: blue
colorTo: purple
sdk: gradio
app_file: app.py
pinned: false

MNIST Digit Recognizer 🔢

A deep learning model that recognizes handwritten digits (0-9) built from scratch using PyTorch and deployed with Gradio on HuggingFace Spaces. ce

🚀 Live Demo

Try it here

📌 Project Overview

This project is part of my 6-month AI Engineer roadmap (Phase 4 — Deep Learning). The model is a Multi Layer Perceptron (MLP) trained on the MNIST dataset of 70,000 handwritten digit images.

🧠 What I Built

  • Built a neural network from scratch using PyTorch
  • Trained on 60,000 images, tested on 10,000 images
  • Achieved ~97% test accuracy
  • Deployed live using Gradio on HuggingFace Spaces

🏗️ Model Architecture

Input → 784 neurons (28×28 flattened) Hidden Layer 1 → 128 neurons + ReLU Hidden Layer 2 → 64 neurons + ReLU Output Layer → 10 neurons (digits 0-9) Loss Function → CrossEntropyLoss Optimizer → Adam (lr=0.001) Epochs → 5

📊 Dataset

  • Name: MNIST
  • Training images: 60,000
  • Test images: 10,000
  • Image size: 28×28 grayscale
  • Classes: 10 (digits 0 to 9)

🛠️ Tech Stack

  • Python
  • PyTorch
  • Gradio
  • HuggingFace Spaces
  • Pillow

📁 Project Structure

app.py → Gradio UI and prediction logic train.py → Model training and saving model.pkl → Trained model weights requirements.txt → Dependencies

💡 Concepts Used

  • Multi Layer Perceptron (MLP)
  • Forward Propagation
  • Backpropagation
  • ReLU Activation Function
  • CrossEntropy Loss
  • Adam Optimizer
  • Gradient Descent

🔗 Connect