Felipe Ortega
LightNews LightNews
jfelipe-ortega.bsky.social
Felipe Ortega
@jfelipe-ortega.bsky.social
21 followers 40 following 4 posts
Associate Professor at EIF, URJC. Coordinator of Bachelor in Data Science and Engineering. Data Engineering at DSLAB-URJC.
Posts Replies Media Videos
jfelipe-ortega.bsky.social
Some wise words in these turbulent times. Learning programming still worths your time and effort: www.deeplearning.ai/the-batch/is... #ai #programming #datascience
DeepSeek-R1 Uncensored, QwQ-32B Puts Reasoning in Smaller Model, and more...
The Batch AI News and Insights: Some people today are discouraging others from learning programming on the grounds AI will automate it.
www.deeplearning.ai
March 22, 2025 at 4:46 PM
jfelipe-ortega.bsky.social
Certainly, we need more studies like this one.
ecolson.bsky.social Eric Colson @ecolson.bsky.social · Feb 6
Operationalizing Machine Learning: An Interview Study by @joehellerstein.bsky.social, @adityagp.bsky.social, et al. Particularly love the part on "Retrofitting Explanations".
#MachineLearning #MLOps #Datascience.
arxiv.org/pdf/2209.09125
February 12, 2025 at 5:48 PM
Reposted by Felipe Ortega
andrew.heiss.phd
Had a blast teaching this today! All the #rstats materials are available at talks.andrewheiss.com/2024-11-13_u...
Go beyond OLS! An introduction to Poisson, Beta, and zero-inflated Beta Bayesian distributional regression While ordinary least squares (OLS) regression is an important method for modeling relationships, it’s not always well-suited for outcome variables that are proportions (bounded between 0–100%) and counts (limited to whole numbers). In this workshop, you’ll learn how to use Bayesian regression techniques to get richer and more detailed information from more specialized types of data. Using R, Stan, {brms}, and {marginaleffects} you’ll learn how to (1) correctly model counts and proportions and (2) process, plot, interpret, and communicate the results. Author Andrew Heiss Affiliation Georgia State University Published November 13, 2024 Workshop presented at the Département de science politique at the Université de Montréal Plot showing the predicted count of anti-LBGT-discrimination laws across a range of urbanness in Democratic, Republican, and swing states, using Bayesian Poisson regression Plot showing the predicted proportion of protected-at-birth tetanus vaccination rates across a range of GDP per capita and 6 different global regions, with vertical lines showing the slopes of the predicted lines for some typical/hypothetical countries, all using Bayesian Beta regression Plot showing multiple Beta distributions across two different parameterizations (shapes and µ+φ)
November 13, 2024 at 10:08 PM
Reposted by Felipe Ortega
datavisfriendly.bsky.social
#rstats With so many folks joining Bsky, it might be useful to repost this note on {matlib} for LaTeX equations in R.
Now at v 1.0.1 on friendly.r-universe.dev.
See the vignette at bit.ly/3yWluZf
November 19, 2024 at 10:55 PM