There are 4 repositories under mnist topic.
A MNIST-like fashion product database. Benchmark :point_down:
Collection of generative models in Tensorflow
Lingvo
Collection of generative models in Pytorch version.
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
Early stopping for PyTorch
Layers Outputs and Gradients in Keras. Made easy.
18 MNIST-like Datasets for 2D and 3D Biomedical Image Classification: pip install medmnist
Experiments for understanding disentanglement in VAE latent representations
Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
A free audio dataset of spoken digits. Think MNIST for audio.
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
TensorFlow2教程 TensorFlow 2.0 Tutorial 入门教程实战案例
Tensorflow implementation of variational auto-encoder for MNIST
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
PyTorch Implementation of CycleGAN and SSGAN for Domain Transfer (Minimal)
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Handwritten digits classification from MNIST with TensorFlow on Android; Featuring Tutorial!
Simple Implementation of many GAN models with PyTorch.
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)
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
A curated list of dedicated resources and applications
Six snippets of code that made deep learning what it is today.
Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10
Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset
A small convolution neural network deep learning framework implemented in c++.
Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.