heretic-li Goto Github PK

algorithms_engineer_interview

计算机视觉算法工程师实习生面试总结（微软、阿里、商汤、海康、华为、平安offer）

business-data-analysis

bilibili啥都会一点的研究生：https://space.bilibili.com/46880349

cao_island

cplusplusthings

C++那些事

deep_1d_velocity

Code for the article : Seismic velocity estimation: a deep recurrent neural-network approach

evolving-cnns-using-ga

Evolving Architectures for Convolutional Neural Networks using the Genetic Algorithm

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_down:

googletest

GoogleTest - Google Testing and Mocking Framework

infer

A static analyzer for Java, C, C++, and Objective-C

multi-scale-attention

Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

nsga-net

NSGA-Net, a Neural Architecture Search Algorithm

psocnn

Code to validate the "Particle swarm optimization of deep neural networks architectures for image classification" paper.

py-r-fcn

R-FCN with joint training and python support

pythonpark

Python 开源项目之「自学编程之路」，保姆级教程：AI实验室、宝藏视频、数据结构、学习指南、机器学习实战、深度学习实战、网络爬虫、大厂面经、程序人生、资源分享。

pytorch-segmentation-multi-models

Pytorch implementation for Semantic Segmentation with multi models (Deeplabv3, Deeplabv3_plus, PSPNet, UNet, UNet_AutoEncoder, UNet_nested, R2AttUNet, AttentionUNet, RecurrentUNet,, SEGNet, CENet, DsenseASPP, RefineNet, RDFNet)

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch-unet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

pytorch_learn

pytorchdocs

PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！

seiscl

Software for viscoelastic full waveform inversion on large heterogeneous clusters

tensorboardx

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

unet-segmentation-pytorch-nest-of-unets

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

unetplusplus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018