Handwritten Digit Classifier
A deep learning model using PyTorch to classify handwritten digits with 98% accuracy.
Project Overview
This project uses a neural network (NN) built with PyTorch to recognize digits from the MNIST dataset. The dataset consists of 28x28 grayscale images.
- Preprocessing and normalization of MNIST dataset
- Implementation of NN with 4 layers
- Training with PyTorch and evaluation
- Visualization of results with Matplotlib
