Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)

A resource-conscious neural network implementation for MCUs

A collection of codes for 'how far can we go with MNIST' challenge

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。

Played with Tensorspace a library for Neural network 3D visualization, building interactive and intuitive models in browsers, supports pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js

This repo contains all the necessary files to build a MNIST TinyML application, that works with an OV7670 camera module and TFT LCD module.

Some basic implementations of Variational Autoencoders in pytorch

I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.

Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

Short python jupyter script, for training Deep learning model for MNIST Dataset about Numbers classification from images and it's evaluation.

Building a model to recognise handwritten numerical digits from images of the MNIST dataset.

Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth

Deep Neural Networks like Single Layer Perceptron and Multi Layer Perceptron implementation using Tensorflow library on Datasets like MNIST and Naval Mine for categorical Classification. Saving and Restoring Tensorflow "Variables" weights for testing.

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

VAE Implementation with LSTM Encoder and CNN Decoder

Handwritten digit recognition using a LeNet-style CNN trained on the MNIST dataset.

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