149 Open source projects that are alternatives of or similar to Bounding-Box-Regression-GUI

Machine Learning Toolkit for Extreme Scale (MaTEx)

Label Embedding Network

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch

The implementation of Relativistic average GAN with Keras

Generative Adversarial Network for MNIST with tensorflow

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

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

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

Tensorflow implementation of conditional variational auto-encoder for MNIST

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

tensorflow implementation of Exploring Randomly Wired Neural Networks for Image Recognition

Lingvo

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）

A curated list of dedicated resources and applications

PyTorch tutorials A to Z

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

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

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

Deep Learning From Scratch

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

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

Implementation of a neural network from scratch in python.

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

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

Layers Outputs and Gradients in Keras. Made easy.

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

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Predict handwritten digits with CoreML

Tensorflow implementation of different GANs and their comparisions

MNIST with TensorFlow Lite on Android

Adventures using keras on Google's Cloud ML Engine

Playing with MNIST. Machine Learning. Generative Models.

Comparison of Generative Models in Tensorflow

Collection of generative models in Pytorch version.

A MNIST-like fashion product database. Benchmark 👇

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

A repository for recording the machine learning code

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

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

An implementation for mnist center loss training and visualization

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

GPU Accelerated t-SNE for CUDA with Python bindings

Simple Implementation of many GAN models with PyTorch.

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

🎎 InfoGAN: Interpretable Representation Learning

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

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。

Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

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.

Minimalist implementation of VQ-VAE in Pytorch

The Pytorch Implementation of L-Softmax

Keras MNIST for Handwriting Detection

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

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

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

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