Stevie Yeh's repositories
IDRiD_Challenge_Mask-RCNN
The Mask RCNN's application in IDRiD(only for challenge 1)
Sequence-Mapping-Bio
This is a code tutorial for rna or dna sequence mapping by myself-RyanYip
Inpainting-with-partial-conv
Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]
ConGAN
Continious Generative Adversarial Network
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
DataScinceJob
The job for science ,wechat
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Eye_Mask_RCNN
Mask RCNN used for Eye dataset.Eye dataset annotation use VGG Image Annotator.
generative_inpainting
Generative Image Inpainting with Contextual Attention https://arxiv.org/abs/1801.07892, demo http://jiahuiyu.com/deepfill
IDRiD-Lesion-Segmentation
The implementation of the segmentation on retinal lesions
Machine-Learning-Web-Apps
Building and Embedding Machine Learning Model into a Web App(With Flask,etc)
Mask-RCNN-series
This series we'll use Mask RCNN
My-Ijulia
My Ijulia learning notebook
NGS-pipeline
By study this, it won't be costly or time-consuming to customize a NGS data analysis pipeline
RNA-Seq-Teaching-O2
This repository has teaching materials for a 2 and 3-day Introduction to RNA-sequencing data analysis workshop using the O2 Cluster
Self-Attention-GAN-Tensorflow
Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
Tensorflow-ObjectDetector-Dr
使用视网膜图片，自己标label，基于ssd模型训练自己的object detector
TextClassification-TF-Demo
CNN-RNN中文文本分类，基于TensorFlow
The-Flask-Mega-Tutorial-zh
翻译自Miguel Grinberg的blog https://blog.miguelgrinberg.com 的2017年新版The Flask Mega-Tutorial教程
the-way-to-go_ZH_CN
《The Way to Go》中文译本，中文正式名《Go 入门指南》
torch-light
Deep-learning by using Pytorch. Basic nns like Logistic, CNN, RNN, LSTM and some examples are implemented by complex model.
UNet-Zoo
A collection of UNet and hybrid architectures in PyTorch for 2D and 3D Biomedical Image segmentation