Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
machine-learning
deep-learning
python
platform
research
finance
algorithmic-trading
auto-quant
fintech
investment
paper
quant
quant-dataset
quant-models
quantitative-finance
quantitative-trading
research-paper
stock-data
Обновлено 2024-11-13 06:41:06 +03:00
Scripts to create debian sysroot image for building electron
Обновлено 2024-08-21 16:06:24 +03:00
ElectionGuard is a set of open source software components that can be used to create and publish end to end verifiable elections as well create a publishable artifact for ballot comparison audits.
Обновлено 2024-08-16 01:30:31 +03:00
ROS package used to create an endpoint to accept ROS messages sent from a Unity scene using the ROS TCP Connector scripts
Обновлено 2024-07-10 10:39:57 +03:00
Command line tool to create corpora for Common Voice
Обновлено 2024-02-12 19:58:34 +03:00
Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
Обновлено 2023-06-12 21:21:58 +03:00
Create waterfall charts with matplotlib
Обновлено 2022-09-09 01:05:48 +03:00
Create Bugzilla attachment comments when new GitHub Pull Requests are created
Обновлено 2022-08-26 18:09:28 +03:00
Create WTML from lists of HiPS datasets
Обновлено 2021-12-27 18:51:34 +03:00
GitHub Action that allows you to attach, create and scale Azure Machine Learning compute resources.
Обновлено 2021-10-19 10:12:10 +03:00
GitHub Action that allows you to create or connect to your Azure Machine Learning Workspace.
Обновлено 2021-10-19 10:11:09 +03:00
Create, list, and load GitHub Gists from Jupyter notebooks
Обновлено 2020-12-01 22:43:21 +03:00
Create a backlog of actionable work items in alignment with various processes based on proven practices.
Обновлено 2020-07-23 05:23:15 +03:00
Lightweight application created for running jobs on demand with a RESTful API interface and a scalable backend. Job queues are based on Zeromq.
Обновлено 2013-07-15 20:58:36 +04:00
INACTIVE - http://mzl.la/ghe-archive - Domesday is a FLOSS tool that can be used by a community to create a directory of members.
Обновлено 2011-06-02 14:37:26 +04:00