Querybook is a Big Data Querying UI, combining collocated table metadata and a simple notebook interface.

A Deep Learning Recommender System

DeepIE: Deep Learning for Information Extraction

Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification

An Efficient RML-Compliant Engine for Knowledge Graph Construction

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

☁️ Build multimodal AI applications with cloud-native stack

The collaborative real-time open-source machine learning devtool and training suite: Experiment execution, tracking, and debugging. With server and project management tools.

A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.

Orquesta is a graph based workflow engine for StackStorm. Questions? https://github.com/StackStorm/st2/discussions

Dev Tools for the Serverless World - Issues, PRs and ⭐️welcome!

List of papers, code and experiments using deep learning for time series forecasting

Perform data science on data that remains in someone else's server

吴恩达老师的机器学习课程个人笔记

《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

NYU Deep Learning Spring 2020

VictoriaMetrics: fast, cost-effective monitoring solution and time series database

preparing new version

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.

MacroBase: A Search Engine for Fast Data

Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

Modular, multi-topic question answering on top of Kubernetes

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

APM, (Application Performance Management) tool for large-scale distributed systems.

Probabilistic time series modeling in Python

A Chinese Translation of Stanford CS229 notes 斯坦福机器学习CS229课程讲义的中文翻译

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Advers…

Istio服务网格进阶实战

Code to 1) scrap wikipedia page view counts, and to 2) conduct time series analysis with GAM