Description

Handwritten Digit Recognition with PyTorch

Overview

This repository provides an end-to-end implementation of a Convolutional Neural Network (CNN) using PyTorch for recognizing handwritten digits from the MNIST dataset. MNIST ("Modified National Institute of Standards and Technology") is a classic dataset in computer vision, widely used for benchmarking classification algorithms. This project aims to demonstrate fundamental techniques in image classification and serves as a practical introduction to neural networks and deep learning.

Features

Data Handling: Utilizes PyTorch's DataLoader and Dataset classes to efficiently handle and preprocess image data.
Model Architecture: Implements a CNN architecture comprising convolutional layers, batch normalization, max pooling, and fully connected layers.
Training: Trains the model using Adam optimizer with a cross-entropy loss function, employing learning rate scheduling to enhance training efficiency.
Evaluation: Evaluates model performance on a validation set and computes classification accuracy metrics.
Inference: Generates predictions on a test set and formats results in a submission-ready CSV file.
Why This Project?

This project is ideal for individuals with some experience in Python and basic machine learning concepts who are looking to delve into computer vision. By working through this repository, you'll gain hands-on experience in:

Building and training a CNN using PyTorch.
Understanding key components of deep learning pipelines such as data loading, model building, training loops, and evaluation.
Practicing with a well-known dataset and comparing performance across different neural network architectures.