A production-grade handwritten digit recognition system built from scratch using deep neural networks. Trains on the MNIST dataset (28×28 pixel images) and achieves up to 99.6% test accuracy using a ...
This project addresses a supervised machine learning problem: recognizing handwritten digits (0–9) from grayscale images. Each image is represented as a vector of pixel intensities and classified ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results