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opcua-asyncio

OPC UA library for python >= 3.7

python-opcua

LGPL Pure Python OPC-UA Client and Server

opcua

Native Go OPC-UA library

UA-.NETStandard

OPC Unified Architecture .NET Standard

tclab_jupyter

A Jupyter based application to explore different control techniques of a simple temperature plant

Python-study-group

PrismHandsOn

Prism for Xamarin.Forms入門 Hands-on

LDATS

Latent Dirichlet Allocation coupled with Bayesian Time Series analyses

TrXFBasicWebClient

matrixprofile-ts

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Neural-Kernel-Network

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

py4chemoinformatics

Python for chemoinformatics

interpret

Fit interpretable models. Explain blackbox machine learning.

marp-vscode

Marp for VS Code: Create slide deck written in Marp Markdown on VS Code

Fast-and-Accurate-Least-Mean-Squares-Solvers

Implementation of the algorithms presented in the paper "Fast and Accurate Least-Mean-Squares Solvers".

swifter

A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

kagglebook

cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Ultra-Light-Fast-Generic-Face-Detector-1MB

💎1MB lightweight face detection model (1MB轻量级人脸检测模型)

shap

A game theoretic approach to explain the output of any machine learning model.

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

AutoEncoder_vs_MetricLearning

AutoEncoder vs Metric Learning for Anomaly Detection

tvart

Time-varying Autoregression with Low Rank Tensors

bqplot

Plotting library for IPython/Jupyter notebooks

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

chainlearn

Mini module with syntax sugar for pandas/sklearn

ds-cheatsheets

List of Data Science Cheatsheets to rule the world

pyod

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

HMM-MAR

Toolbox for segmentation and characterisation of transient connectivity