mnist-classification

Pure python implementation of SNN

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

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

#计算机科学#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）

#安卓#Handwritten digits classification from MNIST with TensorFlow on Android; Featuring Tutorial!

MNIST digit classification with scikit-learn and Support Vector Machine (SVM) algorithm.

PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.

#计算机科学#Handwritten Digit Recognition using Machine Learning and Deep Learning

#计算机科学#Nerual Network of Stochastic Computing for MNIST Recognition

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

A complete neural network built entirely in x86 assembly language that learns to recognize handwritten digits from the MNIST dataset. No frameworks, no high-level languages - just pure assembly from i...

#计算机科学#Generative Adversarial Networks in TensorFlow 2.0

Baseline classifiers on the polluted MNIST dataset, SJTU CS420 course project

TensorFlow implementation of "ResNeSt: Split-Attention Networks"

#计算机科学#A C++ implementation to create, visualize and train Convolutional Neural Networks

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

#计算机科学#Small neural network framework developed in C#, specialized in digit classification (MNIST dataset)

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.