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Azure AI Fundamentals exercises
Lime: Explaining the predictions of any machine learning classifier
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
Microsoft Learn: Cloud & AI Training Content. This repository is public for Instructor Led Training purposes; we do not accept pull requests in this repository.
A game theoretic approach to explain the output of any machine learning model.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Scripts to mirror Github in a cloudy fashion
Library for fast text representation and classification.
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
Workshop on using Mixed Models with R
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Implementation of Authentication Scheme using Gait Signals Captured from Accelerometer (SECRYPT'15)
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explana…
Anomaly detection related books, papers, videos, and toolboxes
RNN based Time-series Anomaly detector model implemented in Pytorch.