Github Trending and Good Stuff 2019-08-10

发布日期: 2019-08-10

阅读次数:

jupyter-notebook

slundberg / shap

A unified approach to explain the output of any machine learning model.
Good Stuff

NHANES survival model with XGBoost and SHAP interaction values - Using mortality data from 20 years of followup this notebook demonstrates how to use XGBoost and shap to uncover complex risk factor relationships.
Census income classification with LightGBM - Using the standard adult census income dataset, this notebook trains a gradient boosting tree model with LightGBM and then explains predictions using shap.
League of Legends Win Prediction with XGBoost - Using a Kaggle dataset of 180,000 ranked matches from League of Legends we train and explain a gradient boosting tree model with XGBoost to predict if a player will win their match.
MNIST Digit classification with Keras - Using the MNIST handwriting recognition dataset, this notebook trains a neural network with Keras and then explains predictions using shap.
Keras LSTM for IMDB Sentiment Classification - This notebook trains an LSTM with Keras on the IMDB text sentiment analysis dataset and then explains predictions using shap.
Explain an Intermediate Layer of VGG16 on ImageNet - This notebook demonstrates how to explain the output of a pre-trained VGG16 ImageNet model using an internal convolutional layer.
Sentiment Analysis with Logistic Regression - This notebook demonstrates how to explain a linear logistic regression sentiment analysis model.
Census income classification with scikit-learn - Using the standard adult census income dataset, this notebook trains a k-nearest neighbors classifier using scikit-learn and then explains predictions using shap.
ImageNet VGG16 Model with Keras - Explain the classic VGG16 convolutional nerual network’s predictions for an image. This works by applying the model agnostic Kernel SHAP method to a super-pixel segmented image.
Iris classification - A basic demonstration using the popular iris species dataset. It explains predictions from six different models in scikit-learn using shap.
shap.dependence_plot

simoninithomas / Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow
Good Stuff

Deep Reinforcement Learning Course
- 🌐 : https://simoninithomas.github.io/Deep_reinforcement_learning_Course/

CamDavidsonPilon / Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka “Bayesian Methods for Hackers”: An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Good Stuff

Bayesian Methods for Hackers
See the project homepage here for examples, too.
Jens Rantil | Kyle Meyer | Eric Martin | Inconditus

MVIG-SJTU / AlphaPose

Real-Time and Accurate Multi-Person Pose Estimation&Tracking System
Good Stuff

AlphaPose is based on RMPE(ICCV’17), authored by Hao-shu Fang, Shuqin Xie, Yu-Wing Tai and Cewu Lu, Cewu Lu is the corresponding author. Currently, it is developed and maintained by Hao-shu Fang, Jiefeng Li, Yuliang Xiu and Ruiheng Chang.

onnx / tutorials

Tutorials for creating and using ONNX models
Good Stuff

ONNX Runtime | Python (Pypi) - onnxruntime and onnxruntime-gpu
C/C# (Nuget) - Microsoft.ML.OnnxRuntime and Microsoft.ML.OnnxRuntime.Gpu| APIs: Python, C#, C, C++
Examples - Python, C#, C |
- Inferencing ONNX models using ONNX Runtime Python API

swift

onevcat / Kingfisher

A lightweight, pure-Swift library for downloading and caching images from the web.
Good Stuff

- - API Reference - Lastly, please remember to read the full whenever you may need a more detailed reference.

Alamofire / Alamofire

Elegant HTTP Networking in Swift
Good Stuff

- Complete Documentation
If you need to find or understand an API, check our documentation or Apple’s documentation for URLSession, on top of which Alamofire is built.

javascript

facebook / create-react-app

Set up a modern web app by running one command.
Good Stuff

User Guide – How to develop apps bootstrapped with Create React App.
Read more about testing.
You can find detailed instructions on using Create React App and many tips in its documentation.
Please refer to the User Guide for this and other information.
An offline-first service worker and a web app manifest, meeting all the Progressive Web App criteria. (Note: Using the service worker is opt-in as of react-scripts@2.0.0 and higher)
The tradeoff is that these tools are preconfigured to work in a specific way. If your project needs more customization, you can “eject” and customize it, but then you will need to maintain this configuration.

facebook / react

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Good Stuff

Learn Once, Write Anywhere: We don’t make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code. React can also render on the server using Node and power mobile apps using React Native.

Wscats / piano

🎹
用键盘8个键演奏一首蒲公英的约定送给996的自己或月亮代表我的心给七夕的她，非常简单~
Good Stuff

体验地址： https://wscats.github.io/piano/build/

material-components / material-components-web

Modular and customizable Material Design UI components for the web
Good Stuff

Demos

xuliangzhan / vxe-table

🐬
一个功能齐全的 Vue 表组件，与任意组件库完美兼容
Good Stuff

aws / aws-sdk-js

AWS SDK for JavaScript in the browser and Node.js
Good Stuff

For browser-based web, mobile and hybrid apps, you can use AWS Amplify Library which extends the AWS SDK and provides an easier and declarative interface.
Alternatively, you can use AWS Amplify Library which extends AWS SDK and provides React Native UI components and CLI support to work with AWS services.
To create React applications with AWS SDK, you can use AWS Amplify Library which provides React components and CLI support to work with AWS services.
AWS Amplify Library provides Angular components and CLI support to work with AWS services.