This is a machine learning model that classifies digits from 0-9 from handwritten digits.

This notebook implements a Quantum Convolutional Neural Network (QCNN) to classify handwritten digits from the MNIST dataset, specifically distinguishing between digits 3 and 6. The implementation uses TensorFlow Quantum (TFQ) and Cirq to create and train a quantum machine learning model.

Classify handwritten digits using machine learning techniques Yan Liang, Yunzhi Wang and Delong Zhao Project scope For our machine learning project, we propose to build several machine learning classifiers that recognize handwritten digits. Handwritten digit recognition is a classic problem in machine learning studies for many years. We plan to do several experiments using different machine learning algorithms and compare the pattern recognition performance. We hope to create a classifier that has same or better categorization accuracy than record performance from previous studies. Yan will focus on neural network, Delong will focus on the random forests methods, and Yunzhi will focus on SVMs and KNNs. We will also develop a final novel classifier that combines the best models from our different experiments. We hypothesize that the final classifier will archive a categorization accuracy of 0.99. This indicates that the classifier correctly classified all the handwritten digits but 1% of the images. The goal of handwritten digit recognition is to determine what digit is from an image of a single handwritten digit. It can be used to test pattern recognition theories and machine learning algorithms. Preprocessed standard handwritten digit image database has been developed to compare different digit recognizers. In our semester project, we will use modified National Institute of Standards and Technology (MNIST) handwritten digit images dataset from kaggle digit recognizer project. The Kaggle MNIST dataset is freely available and collected 28,000 training images and 42,000 test images. Each image is a preprocessed single black and white digit image with 28 x 28 pixels. Each pixel is an integer value range from 0 to 255 which represent the brightness of the pixel, the higher value meaning darker. Each image also has a label which is the correct digit for the handwritten image. For each input handwritten image, our model will output which digit we predict and evaluate with the correct label. We will use 28,000 training images to train our machine learning model and use 42,000 test images to test the performance. Then we will calculate the percentage of the test images that are correctly classified and compare the performance of different machine learning algorithms.

A Convolutional Neural Network (CNN) based project to classify handwritten digits (0–9) using the MNIST dataset. The model is trained and evaluated in Python (TensorFlow / Keras), and its performance is compared with classical machine learning algorithms.

First Machine Learning Challenge: Implement a Perceptron in JavaScript to classify handwritten digits

A machine learning project to classify handwritten digits from the MNIST dataset using an Artificial Neural Network (ANN)

We use some different deep neural networks to classify a handwritten digits date set from UCI. One can also discover the limitation of traditional machine learning models for image classification.

This project focuses on classifying handwritten digits from the MNIST dataset. It explores and compares the performance of various machine learning models including Neural Networks, SVM, and KNN. The project includes data preprocessing, model training and evaluation, and a user-friendly interface for easy interaction and testing.

Apply different machine learning algorithms to classify handwritten digits.

Simple project using dimensionality reduction and machine learning to classify handwritten digit images.

A machine learning application that identifies and classifies handwritten digits (0-9) using a trained deep learning model.

A machine learning project implementing a **Convolutional Neural Network (CNN)** to classify handwritten digits from the MNIST dataset.

A machine learning project for building a classifier for recognizing handwritten hexadecimal digits from images as part of the INF264 course.

A machine learning project that implements handwritten digit recognition using a Convolutional Neural Network (CNN). The system is trained on the MNIST dataset to accurately classify digits (0–9) from images, demonstrating the power of deep learning in image recognition tasks.

Handwritten Digit Prediction - Classification Analysis is to develop a highly accurate machine learning model that can classify handwritten digits (0-9) with precision and recall. The model should generalize well to new data, avoid overfitting, and be deployable in real-world applications, such as optical character recognition systems.

HW1, CS 445, Machine Learning, Winter 2012. A perceptron to classify the handwritten number data at http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits . Generalizable to data with features encoded as integers, with minor variations to code.

Handwritten Image Classification Model This repository contains a machine learning model designed to classify handwritten images (e.g., digits, letters, or symbols). It leverages Convolutional Neural Networks (CNNs) for accurate and efficient classification. Features: Dataset: Trained on the MNIST ,ensuring robust performance

This project aims to incorporate a machine learning model into a web app to classify handwritten digits. Users have to attempt to answer the sum of two numbers by drawing their answers on the canvas.

Trying to use different machine learning methods to classify handwritten digits.

Classifying Optical Recognition of Handwritten Digits dataset using various Machine Learning Algorithms

A simple machine learning algorithm that classifies handwritten digits using RISC-V

Machine learning model that classifies handwritten digits using the SVM (Support Vector Machine) algorithm

A handwritten digits classification system utilizes machine learning to accurately identify and classify handwritten numeric digits (0-9) from images.

Handwritten digit prediction is a machine learning technique that involves training a computer program to recognise and classify handwritten digits.

This project involves implementation of different machine learning algorithms to classify handwritten digits.

Machine Learning model to Recognise & Classify handwritten digits from MNIST Database using kNN Algorithm

Handwritten digit recognition is a machine learning task that identifies digits (0–9) from handwritten images by learning patterns in pixel values and classifying them accurately.

The Hand Written Digit Recognition System Project based on machine learning is capable of recognizing and classifying handwritten digits..

Digit Detection using SVM in Machine Learning. Implementing Support Vector Machine algorithm to classify handwritten digits with scikit-learn. Achieving high accuracy in digit recognition.

An assignment for an introductory Machine Learning course that classifies handwritten digits using K-Means Clustering.