Popular GitHub Repositories for Mnist

Collection of generative models in Pytorch version.

GPU Accelerated t-SNE for CUDA with Python bindings

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

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.

AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集成运行时环境 (IRE)，帮助开发者提升效率。

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

A free audio dataset of spoken digits. An audio version of MNIST.

TensorFlow2教程 TensorFlow 2.0 Tutorial 入门教程实战案例

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

Tensorflow implementation of variational auto-encoder for MNIST

CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art

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

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

A small convolution neural network deep learning framework implemented in c++.

?e?itli kütüphaneler kullan?larak Türk?e kod a??klamalar?yla TEMEL SEV?YEDE pratik derin ??renme uygulamalar?.

MNIST with TensorFlow Lite on Android

The simplest implementation of LeNet5 with mnist in PyTorch. Accuracy: ~99%

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

Convolutional Neural Network with CUDA (MNIST 99.23%)