digit-classification

It is a Python GUI in which you can draw a digit and the ML Algorithm will recognize what digit it is. We have used Mnist dataset

In this part, we developed an interface for Digit Classification using the PyQt5 library in Python.

Building a Neural Network for MNIST Digit Classification from Scratch

This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.

A Django-based web platform that hosts multiple image classification models under one unified interface. Upload an image and get the predicted result instantly.

A numpy implementation of the LeNet-CNN 1998 research paper trained on emnist dataset

An overpowered digit classifier built for EE/CNS/CS 148A @ Caltech. Make digits out of anything.

In this project, I use Keras and TensorFlow to classify digits and python's Tkinter library to visualize

CNN-based MNIST digit classification model implemented in PyTorch Lightning.

Digit Classification using Python

Digit classification task using Naive Bayes, Perceptron, and MIRA.

Handwritten Digit Recognition - Computer Vision Application

Utilizing neural networks to recognize handwritten digits.

A simple feedforward Artificial Neural Network built with PyTorch to classify handwritten digits from the MNIST dataset.

This project implements probabilistic machine learning methods, including Bayesian classification, Gaussian discriminant models, and dropout in neural networks. It explores softmax regression, log-likelihood optimization, and performance evaluation using accuracy, ROC curves, and confusion matrices.

Handwritten digit classification using a fully connected neural network built with TensorFlow and Keras, achieving 98.09% test accuracy on the MNIST dataset.

AI-powered Handwritten Digit Recognition System built using Python, Scikit-Learn and Streamlit with Live Deployment.

Implement digit classification using LibSVM libarary.

A CNN built with PyTorch achieving 98.9% test accuracy on MNIST handwritten digit classification.