278 Open source projects that are alternatives of or similar to KerasMNIST

Generative Adversarial Networks in TensorFlow 2.0

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

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

Lingvo

PyTorch tutorials A to Z

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

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

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）

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

Real-time Number Recognition using Apple's CoreML 2.0 and MNIST -

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

Deep Learning From Scratch

Tensorflow implementation of conditional variational auto-encoder for MNIST

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

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

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch

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

Machine Learning Toolkit for Extreme Scale (MaTEx)

A High Level Python Library to empower students, developers to build applications and systems enabled with computer vision capabilities.

A project designed to explore CNN and the effectiveness of RCNN on classifying the EMNIST dataset.

Generative Adversarial Network for MNIST with tensorflow

Label Embedding Network

Detect Covid-19 with Chest X-Ray Data

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

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Get started with Machine Learning in TensorFlow with a selection of good reads and implemented examples!

🎎 InfoGAN: Interpretable Representation Learning

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

This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference.

Playing with MNIST. Machine Learning. Generative Models.

The Pytorch Implementation of L-Softmax

Simple Implementation of many GAN models with PyTorch.

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

Kervolution implementation using TF2.0

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Minimalist implementation of VQ-VAE in Pytorch

Tensorflow implementation of different GANs and their comparisions

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。

Adventures using keras on Google's Cloud ML Engine

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

Comparison of Generative Models in Tensorflow

Artificial intelligence/machine learning course at UCF in Spring 2020 (Fall 2019 and Spring 2019)

A MNIST-like fashion product database. Benchmark 👇

MNIST with TensorFlow Lite on Android

A repository for recording the machine learning code

Predict handwritten digits with CoreML

Collection of generative models in Pytorch version.

An implementation for mnist center loss training and visualization

Implementation of a digit recognition using my Neural Network with the MNIST data set.

GPU Accelerated t-SNE for CUDA with Python bindings

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

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

TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab.

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

Interactive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.

Keras model and tensorflow optimization of 'improved Training of Wasserstein GANs'

I am taking up the #100DaysOfMLCode Challenge 😎