Production-ready deep learning model for handwritten digit recognition using optimized CNN architecture. Features interactive web app, comprehensive training pipeline, and multiple deployment options. Achieves 99.65% accuracy on MNIST dataset with advanced data augmentation and regularization techniques.

AI digit classifier using pytorch

Implementation of a neural network model with backpropagation algorithmfor image classification using the MNIST dataset, featuring experiments varying learning rates, momentum, hidden units, and training data sizes

Machine learning project for digit recognizer challenge in Kaggle using just numpy, no TF. %85 accuracy

The basic implementation of neural network learning using TensorFlow, It identifies the handwritten digit and displays the result along with confidence.

A feed-forward neural network for digital classification

NeuroAI - Digit Classification Artificial Neural Network

The handwritten Digits Classification model classifies different handwritten digits(from the dataset) to their categorical targets using deep Convolutional neural networks.

This app can classify handwritten digits (0-9) drawn on the screen. It was created by implementing a model coded with Tensorflow in Python and then serving that file via the Tensorflow Inference java library.

Simple NN digit classifier in plain C