peterxiaoguo Goto Github PK

generative-compression

TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

github-slideshow

A robot powered training repository :robot:

github.com-peterxiaoguo-peterxiaoguo.github.io

gnnpapers

Must-read papers on graph neural networks (GNN)

hands_on_ml_with_sklearn_and_tf

OReilly Hands On Machine Learning with Scikit Learn and TensorFlow (Sklearn与TensorFlow机器学习实用指南)

hangzhou_house_knowledge

2017年买房经历总结出来的买房购房知识分享给大家，希望对大家有所帮助。买房不易，且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to everyone.

hkueee

Selected interesting implementations

ib_insync

Python sync/async framework for Interactive Brokers API

iciar2018

Our solution for ICIAR 2018 Grand Challenge

igan

Interactive Image Generation via Generative Adversarial Networks

inkscape-raytracing

An extension for Inkscape that makes it easier to draw optical diagrams.

introtodeeplearning_labs

Lab Materials for MIT 6.S191: Introduction to Deep Learning

iseg2017-nic_vicorob

Implementation of the nic_vicorob team for addressing the MICCAI Grand Challenge on 6-month infant brain MRI segmentation iSeg2017

jindongwang.github.io

Personal website

k-means

A Python implementation of k-means clustering algorithm.

kaggle_ndsb2017

Kaggle datascience bowl 2017

learnopencv

Learn OpenCV : C++ and Python Examples

lits-challenge

INFORMATION FOR THE LIVER TUMOR SEGMENTATION CHALLENGE

lmfit-py

Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize.leastsq, and with many additional classes and methods for curve fitting

lungcancerdetection

Use CNN to detect nodules in LIDC dataset.

lungcancerdetection-1

This project presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest. The experimentation is performed with 15, 000 images obtained from the kaggle contest. The experimental result shows that the proposed CAD system can able to tell the posterior probability of lung cancer for a patient based on the detection algorithm. Also the usage of Random Forest will increase the accuracy of detecting the cancer nodules.

machine-learning-exercises

Machine Learning Course Exercises

machinelearning

Machine Learning in Action（机器学习实战）

mantapatch

mask-rcnn

A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

master_us

:school_satchel: US Graduate Preparation

math

A collection of open source math notes.

medical-imaging-datasets

A list of Medical imaging datasets. Source : https://sites.google.com/site/aacruzr/image-datasets

minfunc

unconstrained optimization tools