MNIST Handwritten Digits Classifier with PyTorch

This repository contains an implementation of a handwritten digits classifier using PyTorch. The model is trained to recognize handwritten digits from the famous MNIST dataset.

Introduction

The goal of this project is to build a neural network-based classifier that can accurately recognize handwritten digits from the MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of hand-drawn digits from 0 to 9.

Requirements

To run the code in this project, you need the following dependencies:

• Python 3
• PyTorch (>= 1.7.0)
• torchvision
• matplotlib
• numpy

Steps to Follow

1. Imports: Import the necessary libraries and modules, including PyTorch, torchvision, and matplotlib.

2. Load the dataset: Load the MNIST dataset using torchvision and create data loaders for both the train and test sets.

3. Justify your preprocessing: Explain the preprocessing steps chosen, such as ToTensor() and Normalize(), and their importance.

4. Explore the dataset: Use matplotlib and torch to explore the data dimensions and view sample images.

5. Build your neural network: Design the neural network architecture using PyTorch's nn.Module.

6. Specify a loss function and an optimizer: Define the loss function and optimizer for training the model.

7. Running your neural network: Train the model using the training data and validate it using the test data.

8. Plot the training loss: Visualize the training loss over epochs to monitor the model's training progress.

9. Testing your model: Evaluate the trained model's performance on the test set and calculate the accuracy.

10. Improving your model: Suggest potential improvements to the model, such as architecture changes or hyperparameter tuning.

11. Saving your model: Save the trained model for future use.

Result

The trained model achieves an accuracy of approximately 97.73% on the test set. This performance can be further improved by fine-tuning hyperparameters or exploring different model architectures.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• The model architecture and training techniques are inspired by various online tutorials and resources.
• Thanks for Udacity Team for giving me the opportunity and necessary resources to work on this project.
• Thanks to the PyTorch community for providing an excellent deep learning framework.