moprescu

CNTK

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CS5220-Final

Class Project for CS5220

CustomModuleDefinition

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.

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.

MAE5810-Final

MAE 5810 Final

mmlspark

Microsoft Machine Learning for Apache Spark

orthogonal_forests

spaCy-tutorial

word2vec-translation

moprescu/CNTK

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

moprescu/CS5220-Final

Class Project for CS5220

moprescu/CustomModuleDefinition

moprescu/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.

moprescu/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.

moprescu/MAE5810-Final

MAE 5810 Final

moprescu/mmlspark

Microsoft Machine Learning for Apache Spark

moprescu/orthogonal_forests

moprescu/spaCy-tutorial

moprescu/word2vec-translation