Pinned Repositories
azure-activedirectory-library-for-python
ADAL for Python
interpret-community
Fit interpretable models. Explain blackbox machine learning.
interpret-text
A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
LightGBM
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
MachineLearningNotebooks
Use the example notebooks in this repo to explore the Azure Machine Learning service.
ml-wrappers
A unified wrapper for various ML frameworks - to have one uniform scikit-learn format predict and predict_proba functions.
responsible-ai-toolbox
This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
shap
A unified approach to explain the output of any machine learning model
spark
Mirror of Apache Spark
SynapseML
Microsoft Machine Learning for Apache Spark
imatiach-msft's Repositories
imatiach-msft/ml-wrappers
A unified wrapper for various ML frameworks - to have one uniform scikit-learn format predict and predict_proba functions.
imatiach-msft/interpret-text
A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
imatiach-msft/LightGBM
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
imatiach-msft/MachineLearningNotebooks
Use the example notebooks in this repo to explore the Azure Machine Learning service.
imatiach-msft/responsible-ai-toolbox
This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
imatiach-msft/SynapseML
Microsoft Machine Learning for Apache Spark
imatiach-msft/interpret-community
Fit interpretable models. Explain blackbox machine learning.
imatiach-msft/shap
A unified approach to explain the output of any machine learning model
imatiach-msft/spark
Mirror of Apache Spark
imatiach-msft/azure-sdk-for-python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.
imatiach-msft/azureml-assets
imatiach-msft/azureml-examples
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
imatiach-msft/Azureml-ResponsibleAI-Preview
Private Preview: Responsible AI Tooling in Azure Machine Learning
imatiach-msft/AzureMLResponsibleAI
A repository to hold information about the preview of Azure Machine Learning's Responsible AI and model evaluations tools
imatiach-msft/d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
imatiach-msft/DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
imatiach-msft/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.
imatiach-msft/fairlearn
A Python package that implements a variety of algorithms that mitigate unfairness in supervised machine learning.
imatiach-msft/fibberio
imatiach-msft/fluentui
Fluent UI web represents a collection of utilities, React components, and web components for building web applications.
imatiach-msft/human-eval
Code for the paper "Evaluating Large Language Models Trained on Code"
imatiach-msft/interpret
Fit interpretable models. Explain blackbox machine learning.
imatiach-msft/lime
Lime: Explaining the predictions of any machine learning classifier
imatiach-msft/nvm-windows
A node.js version management utility for Windows. Ironically written in Go.
imatiach-msft/PSI-Library
R library of differentially private algorithms for exploratory data analysis
imatiach-msft/python-tabulate
Repository migrated from https://bitbucket.org/astanin/python-tabulate
imatiach-msft/ResponsibleAI-Airlift
Given the importance of responsible development and deployment of AI systems, the goal of this session is to equip you with the knowledge you need to successfully use, apply, and promote these offerings on customer use cases. Please forward this invite to other solution architects, evangelists, field activators, etc. We truly hope we can limit this workshop to those of you who work directly with customers on ML and AI topics.
imatiach-msft/seismic-deeplearning
Deep Learning for Seismic Imaging and Interpretation
imatiach-msft/sparklyr
R interface for Apache Spark
imatiach-msft/vision-explanation-methods
Methods for creating saliency maps for computer vision models.