Classify MNIST image dataset into 10 classes. Build an image classifier with Recurrent Neural Network (RNN: LSTM) on Tensorflow.

An Intuitive Desktop GUI Application For Recognizing Multiple Handwritten Digits Drawn At The Same Time. Trained On MNIST Dataset and Built With Python, OpenCV and TKinter

A tool to generate image dataset for sequences of handwritten digits using MNIST database

A Web Application Built with Flask and Python that reads images containing numbers with the Help of Tensor-flow should recognize each digit from 0 to 9

MNIST accelerator using pynq-z2 and the binary qunatization

This dataset has 10 food categories, with 5,000 images. For each class, 125 manually reviewed test images are provided as well as 375 training images. All images were rescaled to have a maximum side length of 512 pixels.

Tensorflow Quick Introduction

A Generative Adversarial Network (GAN) trained on the MNIST dataset, capable of creating fake but realistic looking MNIST digit images that appear to be drawn from the original dataset.

Derin Öğrenme kullanarak el yazısıyla yazılmış rakamları tanımak için yazılmış bir Flask uygulamasıdır.

I created a Deep Learning Model which can do multiple task at a same time using tensorflow and keras.

"THE MNIST DATABASE of handwritten digits" DataReader http://yann.lecun.com/exdb/mnist/

Convert data in IDX format in MNIST Dataset to Numpy Array using Python

Extract MNIST handwritten digits dataset binary file into bmp images

Download and load MNIST data from R

MNIST handwritten digit classification using PyTorch

基于TibetanMNIST图像分类与图像匹配

simple neural network with simple project

DigiPic-Classifier is a powerful image classification app built with Streamlit. It features two models: CIFAR-10 Object Recognition to classify objects like airplanes, cars, animals, and more, and MNIST Digit Classification for recognizing handwritten digits. With a sleek interface and real-time predictions, DigiPic-Classifier offers a seamless

Implementing GAN for two different datasets with PyTorch.

Detecting digits on the image using Convolution Neural Networks