agriculture_knowledgegraph

农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策

ausenergyviz

Python and R scripts to download and visualise Australian electricity market data

awesome-cpp-cn

C++ 资源大全中文版，标准库、Web应用框架、人工智能、数据库、图片处理、机器学习、日志、代码分析等

awesome-machine-learning-cn

机器学习资源大全中文版，包括机器学习领域的框架、库以及软件

awesome-python-cn

Python资源大全中文版，包括：Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等，由伯乐在线持续更新。

bert-bilstm-crf

使用BERT-BILSTM-CRF进行中文命名实体识别。

bert4keras4nlp

基于bert4keras进行nlp工作，暂时包含文本分类，实体识别，实体关系抽取

books

"我的阅历"

cee_272r

chainknowledgegraph

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体，包括上市公司所属行业关系、行业上级关系、产品上游原材料关系、产品下游产品关系、公司主营产品、产品小类共6大类。 上市公司4,654家，行业511个，产品95,559条、上游材料56,824条，上级行业480条，下游产品390条，产品小类52,937条，所属行业3,946条。

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

electricity_load_forecast

Electricity Load and Price Forecasting

em

electricity market

forecasting

A set of Prediction algorithms for forecasting time series data

freeml

Data Science Resources (Mostly Free)

knowledge_graph_demo

本项目用于展示三元组抽取后形成的知识图谱，包括几本小说的实体关系，以及README.md，介绍这方面的一篇文章。

lansexiaobang

load-forecasting

Forecasting average total load on the Elia electric grid via machine learning

machine-learning-course

R code for the assignments of Coursera machine learning course

machine-learning-notes

This contains my past machine learning notes

machinelearning_chinahadoop

:shell: 小象学院机器学习

marima

:exclamation: This is a read-only mirror of the CRAN R package repository. marima — Multivariate ARIMA and ARIMA-X Analysis

mlssvr

Multi-output Least-Squares Support Vector Regressor

mpec

Simulations codes for the paper titled "Strategic Bidding for Producers in Nodal Electricity Markets: A Convex Relaxation Approach"

pglib-opf

Benchmarks for the Optimal Power Flow Problem

power-systems-optimization

Implementation of Optimization Techniques for Economic Dispatch of Power Systems

practical-programming-books

这里收录比较实用的计算机相关技术书籍，可以在短期之内入门的简单实用教程、一些技术网站以及一些写的比较好的博文，欢迎Fork，你也可以通过Pull Request参与编辑。

pyramid

Pyramid brings R's beloved auto.arima to Python, making an even stronger case for why you don't need R for data science. It does so not by calling R under the hood, but by wrapping statsmodels' well-tested ARIMA and SARIMAX estimators in a single, easy-to-use scikit-learn-esque estimator.

pytoneo4j

Python创建Neo4J知识图谱数据库

reinforcement_learning_financial_trading

MATLAB example on how to use Reinforcement Learning for developing a financial trading model