Handwritten_Digit_Recognition_With_LeNet5_Using_Pytorch

Project Title

Handwritten Digit Recognition with LeNet5 Model in PyTorch

Introduction

Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning. In this project, we propose to implement the LeNet5 model using PyTorch to recognize handwritten digits from the MNIST dataset. The goal is to build an accurate digit recognition system that can classify digits with high accuracy.

Expected Deliverables

1. Implementation of various models for handwritten digit recognition, including MLP, CNN, and LeNet5.
2. Evaluation reports comparing the performance of different models in terms of accuracy and computational efficiency.
3. Visualizations of model predictions and evaluation metrics.
4. Documentation detailing the methodology, implementation details, and findings of the project