| --- |
| title: MNIST Digit Recognizer |
| emoji: 🔢 |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| app_file: app.py |
| pinned: false |
| --- |
| # MNIST Digit Recognizer 🔢 |
|
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| 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](https://jayeshmurthi49-byte-mnist-digit-recognizer.hf.space) |
|
|
| ## 📌 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 |
| - GitHub: https://github.com/jayeshmurthi49-byte |
| - LinkedIn: https://linkedin.com/in/jayesh-murthi-8b1653400 |
