fedorajzf,github

deepsurv

DeepSurv is a deep learning approach to survival analysis.

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

difacto

Distributed Factorization Machines

differential-equations

Jupyter notebooks on differential equations and how to solve them using Python.

diffgeoops

This repository contains a Python implementation of the paper "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds" by Meyer et. al. VisMath 2002

dirt-t

A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)

discrete_sieve

The information sieve for discrete variables.

distributionally-robust-optimization

Experiments with distributionally robust optimization (DRO) for deep neural networks

dl4h

Deep learning for hackers: a hands-on approach to machine learning and deep learning.

dmlc-core

A common code-base for Distributed Machine Learning in C++

dmtk

Microsoft Distributed Machine Learning Tookit

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.

downhill

Stochastic gradient optimization routines for Theano

dsr

Deep Successor Representation

e2e-model-learning

Task-based end-to-end model learning in stochastic optimization

ebgan

A tensorflow implementation of Junbo et al's Energy-based generative adversarial network ( EBGAN ) paper.

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.