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

AI digit classifier using pytorch

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

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.

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.

NeuroAI - Digit Classification Artificial Neural Network

A feed-forward neural network for digital classification

Simple NN digit classifier in plain C