kvasilaky Goto Github PK

amnesty

11/15 NYC DataDive Amnesty International project

angular-meteor-forum-tutorial-master

https://github.com/fuloptibi/angular-meteor-forum-tutorial

anotherform_deployed

Part 3: Baby flask app with postgres db deployed on Heroku

dggit

econ508

econ552

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.

ethiopiasms

fate_sigir

Modules for running online reranking experiments, as presented by InfoSeeking lab at SIGIR 2021.

great

gwc_viacom

inverseproblem

This function inverts ill conditioned matrices using an iterative solution to the Tikhonov regularization problem. It takes three arguments: A, the matrix, l, lambda the contraint, and k, the number of iterations. In this iterative Tikhonov regularization model, also known as ridge regression, I introduce an iterative solution to the ill-posed linear inverse problem. My approach to the inverse problem can be viewed as a generalization of existing methods, where, in addition to the regularization parameter, I introduce a second regularization parameter as the number if iterations. This work is motivated by the fact that the least squares solution does not give a reasonable result when the data matrix is singular or ill-conditioned. Test cases show that the approach is either better or significantly better than existing L2 regularization methods.

katya_flask_tutorial

This is yet another Flask tutorial in the world, but my aim is explain the hidden brain of flask a bit more pedantically for non software developers. I break down the tutorial in 3 parts, module, package, app. Accompanying ipynb still to come with detailed comments.

kvasilaky.github.io

Kathryn (Katya) Vasilaky

lmpy

The lmpy package implements basic linear regression and bootstrapping functions

melbourne-housing-data-kaggle

This repo uses the Kaggle data set of Melbourne Housing Market. https://www.kaggle.com/anthonypino/melbourne-housing-market

pydata

Meetup material November 14, 2018

pyladies

raspberrypi

Pycon2017, Accompanying files to raspberrypi water sensor tutorial

rworkshop

tinkersconstruct

Tinker a little, build a little, tinker a little more...