369 Open source projects that are alternatives of or similar to Fun-with-MNIST

Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

Repository for implementation of generative models with Tensorflow 1.x

でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet

Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)

wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

Simple Implementation of many GAN models with PyTorch.

Minimalist implementation of VQ-VAE in Pytorch

Machine learning, in numpy

Tensorflow implementation of different GANs and their comparisions

Comparison of Generative Models in Tensorflow

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Awesome Generative Adversarial Networks with tensorflow

A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.

Implementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN

Tensorflow implementation of variational auto-encoder for MNIST

My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]

TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.

Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images

Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.

DCGAN LSGAN WGAN-GP DRAGAN Tensorflow 2

A repository for recording the machine learning code

There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

GANs Implementations in Keras

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Pure tensorflow implementation of progressive growing of GANs

Tensorflow Implementation of Paper "Improved Training of Wasserstein GANs"

Tensorflow 2.0 implementation of Adversarial Autoencoders

Tensorflow implementation of adversarial auto-encoder for MNIST

A minimal implementation of Wasserstein GAN

Experiments for understanding disentanglement in VAE latent representations

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Tensorflow implementation of conditional variational auto-encoder for MNIST

The Pytorch Implementation of L-Softmax

No description or website provided.

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

TI-pooling: transformation-invariant pooling for feature learning in Convolutional Neural Networks

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Maximum Covariance Analysis in Python

Predict handwritten digits with CoreML

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

Implement Decoupled Neural Interfaces using Synthetic Gradients in 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）

Lingvo

Adventures using keras on Google's Cloud ML Engine

Pytorch implementation of contractive autoencoder on MNIST dataset

MNIST with TensorFlow Lite on Android

Machine Learning Toolkit for Extreme Scale (MaTEx)

PyTorch tutorials A to Z

Generative Adversarial Network for MNIST with tensorflow

A MNIST-like fashion product database. Benchmark 👇