A deep learning project for classifying handwritten digits using TensorFlow and storing results in a PostgreSQL database. This project includes scripts for image processing, deep learning model development, and database management.

A "Hello World" ML neural network project features a FastAPI docker image for digit predictions and a React frontend where users can draw digits to see instant predictions

identify digits from MNIST dataset of tens of thousands of handwritten images

The MNIST dataset was used to train a neural network having a single linear layer with SoftMax employed in the criterion function (Cross Entropy Loss) to classify handwritten digits in classes 0 to 9. The model yielded a 92% accuracy on the MNIST test dataset in 10 training epochs.

This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting.

Web interface to a Convolutional Neural Network (CNN) for classifying handwritten digits

This project uses autoencoders to denoise MNIST images, aiming to improve handwritten digit recognition by refining classifier training data

Handwritten Digit Classification on MNIST Dataset, Utilising Only Traditional Machine Learning Techniques and a Custom Feature Extractor, achieving highest accuracy of 98.08% with the same.

A simple project that detects handwritten digits with keras

The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different classifier model

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

A simple neural network emulator of a multilayer perceptron without using external library methods for classification digits (0, 1, 2 and 3) represented as an array of 0 and 1.

Simple MNIST Handwritten Digit Classification using Pytorch

Code and data for the Digit Recognizer competition on Kaggle.

TensorFlow2 digits classification - Linear Classifier and MLP

It is about implementing KNN(K nearest neighbor) on Mnist dataset which contains digit images

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

Designd a ML Model from the MNIST dataset to identify digit classification using the SVM algorithm.

Classification of digits based on their Audio Inputs.

I have implemented a Conv2d algo to classify the hand made digits data which can be found on Kaggle . Got an accuracy of 99.76. To download the data for this model go to https://www.kaggle.com/c/digit-recognizer