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caffe2

Caffe2 is a lightweight, modular, and scalable deep learning framework.

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

dcgan

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep_residual_unet

Keras implementation of Road Extraction by Deep Residual U-Net article

deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

deepunet

deepUnet architecture image segmentation model as part of Kaggle carvana competition. This single model gives 0.9964 score on IoU metrics.

detect-cell-edge-use-unet

Unet

fcn.tensorflow

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

fusion-net

fusionnet-pytorch

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

gloo

Collective communications library with various primitives for multi-machine training.

google_inception_v3_for_caffe

Google Inception (deepdream) v3 for Caffe

hello-world

This is my first GitHub repository

hide

A package for simulating of a single dish radio telescope survey

image-augmentor

ivy

Simple and flexible workflow engine

learntensorflow

libqtshadowsocks

A lightweight and ultra-fast shadowsocks library written in C++14 with Qt framework

models

Models and examples built with TensorFlow

neural-style-tf

TensorFlow (Python API) implementation of Neural Style

parallel-computing-training

这是我学习并行计算的代码文件夹

pythonconvertcifar-10

convert cifar-10 dataset from bin to png or jpg

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rdma-tutorial

A tutorial on RDMA based programming using code examples

resnet-cifar10-caffe

ResNet 20 32 44 56 110 for CIFAR10 with caffe

rfi-net

rnn-astronomy-signal-process

seek

A flexible and easy-to-extend data processing pipeline for single dish radio telescopes

semantic-segmentation-with-adversarial-networks

Tensorflow implementation of the paper "Semantic Segmentation using Adversarial Networks" by Luc et al.