All Projects → matex-org → Matex

matex-org / Matex

Licence: other
Machine Learning Toolkit for Extreme Scale (MaTEx)

Programming Languages

c
50402 projects - #5 most used programming language

Projects that are alternatives of or similar to Matex

numpy-neuralnet-exercise
Implementation of key concepts of neuralnetwork via numpy
Stars: ✭ 49 (-52.88%)
Mutual labels:  mnist, deeplearning
Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Stars: ✭ 11,943 (+11383.65%)
Mutual labels:  deeplearning, mpi
Gordon cnn
A small convolution neural network deep learning framework implemented in c++.
Stars: ✭ 241 (+131.73%)
Mutual labels:  deeplearning, mnist
VAE-Gumbel-Softmax
An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
Stars: ✭ 66 (-36.54%)
Mutual labels:  mnist, deeplearning
Capsnet
CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art
Stars: ✭ 423 (+306.73%)
Mutual labels:  deeplearning, mnist
Relativistic Average Gan Keras
The implementation of Relativistic average GAN with Keras
Stars: ✭ 36 (-65.38%)
Mutual labels:  deeplearning, mnist
Androidtensorflowmnistexample
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
Stars: ✭ 449 (+331.73%)
Mutual labels:  deeplearning, mnist
Polyaxon Examples
Code for polyaxon tutorials and examples
Stars: ✭ 57 (-45.19%)
Mutual labels:  deeplearning, mpi
Specgan
SpecGAN - generate audio with adversarial training
Stars: ✭ 92 (-11.54%)
Mutual labels:  deeplearning
Intra Bag And Inter Bag Attentions
Code for NAACL 2019 paper: Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Stars: ✭ 98 (-5.77%)
Mutual labels:  deeplearning
Lipreading Densenet3d
DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990
Stars: ✭ 91 (-12.5%)
Mutual labels:  deeplearning
Classification Of Hyperspectral Image
Classification of the Hyperspectral Image Indian Pines with Convolutional Neural Network
Stars: ✭ 93 (-10.58%)
Mutual labels:  deeplearning
Har Keras Cnn
Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras
Stars: ✭ 97 (-6.73%)
Mutual labels:  deeplearning
Micromlp
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
Stars: ✭ 92 (-11.54%)
Mutual labels:  deeplearning
Sert
Semantic Entity Retrieval Toolkit
Stars: ✭ 100 (-3.85%)
Mutual labels:  deeplearning
Pytorch Seq2seq
An open source framework for seq2seq models in PyTorch.
Stars: ✭ 1,297 (+1147.12%)
Mutual labels:  deeplearning
Wb color augmenter
WB color augmenter improves the accuracy of image classification and image semantic segmentation methods by emulating different WB effects (ICCV 2019) [Python & Matlab].
Stars: ✭ 89 (-14.42%)
Mutual labels:  deeplearning
Ytk Mp4j
Ytk-mp4j is a fast, user-friendly, cross-platform, multi-process, multi-thread collective message passing java library which includes gather, scatter, allgather, reduce-scatter, broadcast, reduce, allreduce communications for distributed machine learning.
Stars: ✭ 102 (-1.92%)
Mutual labels:  mpi
Androidtensorflowmachinelearningexample
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
Stars: ✭ 1,369 (+1216.35%)
Mutual labels:  deeplearning
The Nlp Pandect
A comprehensive reference for all topics related to Natural Language Processing
Stars: ✭ 1,349 (+1197.12%)
Mutual labels:  deeplearning

MaTEx: Machine Learning Toolkit for Extreme Scale

MaTEx is a collection of high performance parallel machine learning and data mining (MLDM) algorithms, targeted for desktops, supercomputers and cloud computing systems.

Getting Started

Please look at the MaTEx wiki: https://github.com/matex-org/matex/wiki for detailed instructions and support.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].