Hand Digit Recognition

This project is focused on creating a machine learning model for hand-written digit recognition using the MNIST dataset. It involves implementing a classification model in PyTorch, where the model is trained to predict digits that are hand-written. The dataset is sourced from the MNIST database and is available through PyTorch’s torchvision.dataset.

The model uses techniques like the softmax function to convert the output of the neural network into probabilities, and cross-entropy loss to evaluate the model’s performance. During the project, data preprocessing is performed to clean the dataset and ensure it is in the proper format for training the model, which improves the model’s accuracy.

The code for this project is written in Python 3.7, and it includes all necessary dependencies specified in a requirements.txt file. To set up the project, users need to clone the repository, install the required libraries, and run the Python script to train and test the model. The results show excellent accuracy in digit classification, demonstrating the success of the model.