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

一个基于支持向量机（SVM）模型的手写数字识别系统。使用了经典的MNIST数据集，通过图像预处理、特征提取和模型训练等步骤，构建了一个可以识别0到9之间数字的分类器。并使用Gradio搭建简单的可视化界面，方便用户上传手写数字图像并查看预测结果。

Deep learning demos using MNIST data set with multiple neural network models

VAE Implementation with LSTM Encoder and CNN Decoder

Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.

A Convolutional neural network heavily based upon the tensorflow advanced MNIST example but equiped with labels to visualize and allowing the user to draw an image and then have the system predict the result.

CNN model for predicting random input.

Dockerize a Keras CNN model, which is wrapped in a Webapp using Flask Micro Framework

PyTorch implementation of a feed forward neural network to classify handwritten digits from the MNIST dataset