How niteshksah/mnist-handwritten-digit-classifier-pytorch works
This is a PyTorch MNIST digit-classification project. Its documented workflow explores model architectures and training variations, compares validation accuracy, and supports interactive handwritten-digit prediction. The only source file is a notebook, and no source excerpts were provided, so this map reflects README-level capabilities; internal call relationships cannot be verified.
Main entry points: Neu_Net.ipynb Model experiments
Components 7
MNIST workflow
- MNIST dataset
- MLP models
Neu_Net.ipynb - CNN model
Neu_Net.ipynb - Model experiments
Neu_Net.ipynb - Accuracy comparison
Neu_Net.ipynb - Interactive prediction
Neu_Net.ipynb
Other components
- Digit user
Connections 7
- MNIST dataset → Model experiments: evaluates on
- Model experiments → MLP models: varies architecture
- Model experiments → CNN model: tests CNN
- Model experiments → Accuracy comparison: compares accuracy
README.md - Digit user → Interactive prediction: submits digits
README.md - Interactive prediction → MLP models: classifies with
- Interactive prediction → CNN model: classifies with