/MNIST-Digit-classification

This project involves recognising handwritten digits from MNIST Dataset from UCI ML repository by implementing perceptron learning algorithm on 10 perceptrons(single layer Neural Network) and multilayer Neural Network.

Primary LanguagePython

MNIST-Digit-classification using Perceptron Learning algorithm

Overview

Recognised handwritten digits from MNIST Dataset by implementing perceptron learning algorithm

MNIST Dataset

Methodology

Trained 10 perceptrons that as a group learned to classify the handwritten digits in the MNIST dataset. Each perceptron has 785 inputs and one output. Each perceptron’s target is one of the 10 digits, 0−9. The inputs for 785 consisits of 784 pixels representing 28 X 28 pixel image represented as gray scaled value 0-255 for single handwritten digit. The output of each perceptron is either 0 / 1 and each of these perceptrons learns using the perceptron learning algorithm