michaelespero

michaelespero's Stars

• Lightning-AI/lightning

  Deep learning framework to train, deploy, and ship AI products Lightning fast.

  Language:Python25.3k2446.6k3.1k

• PAIR-code/facets

  Visualizations for machine learning datasets

  Language:Jupyter Notebook7.3k267159887

• uber/causalml

  Uplift modeling and causal inference with machine learning algorithms

  Language:Python5k84395771

• facebookresearch/hiplot

  HiPlot makes understanding high dimensional data easy

  Language:TypeScript2.7k2889141

• mitmath/computational-thinking

  Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia

  Language:Julia2.6k10842479

• allisonhorst/stats-illustrations

  R & stats illustrations by @allison_horst

  1.8k10611199

• denizyuret/Knet.jl

  Koç University deep learning framework.

  Language:Jupyter Notebook1.4k80495230

• mjskay/tidybayes

  Bayesian analysis + tidy data + geoms (R package)

  Language:R7252430759

• ModelOriented/DrWhy

  DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

  Language:R68035985

• rvlenth/emmeans

  Estimated marginal means

  Language:R3581247931

• JuliaStats/HypothesisTests.jl

  Hypothesis tests for Julia

  Language:Julia2962214487

• amueller/COMS4995-s20

  COMS W4995 Applied Machine Learning - Spring 20

  Language:Jupyter Notebook245173114

• madeleineudell/LowRankModels.jl

  LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.

  Language:Julia190166765

• topepo/rstudio-conf-2019

  Slide, code and data for "Applied Machine Learning" at Rstudio-conf 2019

  Language:HTML14927081

• mattansb/Structural-Equation-Modeling-foR-Psychologists

  Lesson files used in the Structural Equation Modeling for Psychologists.

  Language:R13481930

• matloff/regtools

  Various tools for linear, nonlinear and nonparametric regression.

  Language:R12412634

• michaelhallquist/MplusAutomation

  The MplusAutomation package leverages the flexibility of the R language to automate latent variable model estimation and interpretation using Mplus, a powerful latent variable modeling program developed by Muthen and Muthen (www.statmodel.com). Specifically, MplusAutomation provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.

  Language:R84916946

• angusmoore/Matte.jl

  Julia-powered dashboards, inspired by Material Design

  Language:Julia722262

• xorJane/Introduction_to_Julia_tutorials

  These are the jupyter notebooks used for intro tutorials to teach Julia

  Language:Jupyter Notebook707247

• SISBID/Data-Wrangling

  Teaching material for Summer Institute in Statistics for Big Data Module 1.

  Language:HTML61108185

• cardiomoon/processR

  Language:R5452110

• pbiecek/XAIatERUM2020

  Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

  Language:R529111

• SeanPLeary/time-series-h2o-automl-example

  Time-series forecasting using H2O's AutoML example

  Language:Jupyter Notebook484218

• ORIE4741/demos

  Demos and tutorials for ORIE 4741

  Language:Jupyter Notebook3415079

• SISBID/All-Modules-Archive

  Language:HTML236026

• tunsmart/interactionR

  An R package for full reporting of effect modification and interaction

  Language:R181206

• AashitaK/A-Hands-on-Workshop-series-in-Machine-Learning-Fall-2019

  Language:Jupyter Notebook83010

• kkane5/USC_PLUS668_R

  Language:R1101

• mwsohn/Paramed.jl

  Counterfactual mediation analysis

  Language:Julia1200

• satyajeet29/SWB

  Build code for Twitter project with SWB

  Language:Python10