mnist-classification
There are 901 repositories under mnist-classification topic.
Shikhargupta/Spiking-Neural-Network
Pure python implementation of SNN
amitshekhariitbhu/AndroidTensorFlowMNISTExample
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
dougbrion/pytorch-classification-uncertainty
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
zhengyima/mnist-classification
Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）
hereismari/mnist-android-tensorflow
Handwritten digits classification from MNIST with TensorFlow on Android; Featuring Tutorial!
ksopyla/svm_mnist_digit_classification
MNIST digit classification with scikit-learn and Support Vector Machine (SVM) algorithm.
ylsung/pytorch-adversarial-training
PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
anujdutt9/Handwritten-Digit-Recognition-using-Deep-Learning
Handwritten Digit Recognition using Machine Learning and Deep Learning
hwalsuklee/tensorflow-mnist-cnn
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
leamoon/StochasticNet
Nerual Network of Stochastic Computing for MNIST Recognition
mil-ad/snip
Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.
shaohua0116/MultiDigitMNIST
Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning
tlatkowski/gans-2.0
Generative Adversarial Networks in TensorFlow 2.0
cxy1997/MNIST-baselines
Baseline classifiers on the polluted MNIST dataset, SJTU CS420 course project
YeongHyeon/ResNeSt-TF2
TensorFlow implementation of "ResNeSt: Split-Attention Networks"
albertnadal/Tensar
A C++ implementation to create, visualize and train Convolutional Neural Networks
hwalsuklee/how-far-can-we-go-with-MNIST
A collection of codes for 'how far can we go with MNIST' challenge
hwalsuklee/tensorflow-mnist-MLP-batch_normalization-weight_initializers
MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.
aliakbar09a/mnist_digits_classification
Digit Classifier trained on MNIST and tested using webcam.
mcabinaya/Digital-Image-Processing
My works for EE 569 - Digital Image Processing - Spring 2018 - Graduate Coursework at USC - Dr. C.-C. Jay Kuo
floydhub/mnist
Pytorch mnist example
Yangyangii/DANN-pytorch
Implementation of DANN with pytorch
jaehosung/tensorflow-mnist-tutorial
MNIST classification in Tensorflow using Django
Sanjana7395/static_quantization
Post-training static quantization using ResNet18 architecture
BlackHC/mnist_by_zip
Compression algorithms (like the well-known zip file compression) can be used for machine learning purposes, specifically for classifying hand-written digits (MNIST)
xuankuzcr/robomaster_mnist
[RoboMaster2017] Handwritten numerals and digital tube recognition.
bat67/TibetanMNIST
MNIST of Tibetan handwriting 国产手写藏文MNIST数据集（TibetanMNIST）的图像分类处理与各种好玩的脑洞~
cmasch/zalando-fashion-mnist
Evaluation of fashion-MNIST with a simple cnn
nipunmanral/MLP-Training-For-MNIST-Classification
Multilayer perceptron deep neural network with feedforward and back-propagation for MNIST image classification using NumPy
avik-pal/RegNeuralDE.jl
Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)
EN10/KerasMNIST
Keras MNIST for Handwriting Detection
MainakRepositor/Deep-Learning-Python
A collection of some cool deep learning projects in python
DebasmitaGhose/PyTorch_Graph_Neural_Network_MNIST
Example code to train a Graph Neural Network on the MNIST dataset in PyTorch for Digit Classification
taavishthaman/LeNet-5-with-Keras
Implementation of LeNet-5 with keras
arm-on/interpretable-image-classification
Interpretability methods applied on image classifiers trained on MNIST and CIFAR10