Pinned Repositories
azure-docs
Open source documentation of Microsoft Azure
CSharpResources
DataScienceResources
This repository houses some of the links which I found useful for data science and machine learning.
DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
interpret-community
The Interpret Community extends Interpret repo with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
PythonPLResources
Repository for resources on python programming and related frameworks
DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
ml-wrappers
A unified wrapper for various ML frameworks - to have one uniform scikit-learn format for predict and predict_proba functions.
responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
gaugup's Repositories
gaugup/DataScienceResources
This repository houses some of the links which I found useful for data science and machine learning.
gaugup/gaugup
Config files for my GitHub profile.
gaugup/azure-docs
Open source documentation of Microsoft Azure
gaugup/CSharpResources
gaugup/DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
gaugup/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
gaugup/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.
gaugup/interpret-community
The Interpret Community extends Interpret repo with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
gaugup/PythonPLResources
Repository for resources on python programming and related frameworks
gaugup/fairlearn
A Python package to assess and improve fairness of machine learning models.
gaugup/imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
gaugup/PythonPerformanceResources
This repository houses resources for debugging performance issues and improving performance of python programs.
gaugup/TrivializeConda
Often conda operations to create and manage conda operations may become challenging if you have too many conda virtual environments. This repo creates a assistant for you using which you can manage the conda related operations more easily.
gaugup/TypescriptResources
Repository for useful material on Typescript programming language.