πΌοΈ CSS layout: I only came across Every Layout @layoutplusplus recently, but the content that's published so far looks amazing. The mission: "teach you how to better harness the built-in algorithms that power browsers and CSS". https://t.co/UoJ8sSR9W0 pic.twitter.com/b0AsJZKqso
β Ines Montani γ°οΈ (@_inesmontani) June 27, 2019
Someone asked me for tips in the comments, so I might as well make this a thread. Here are my personal recommendations for how to get started with front-end development, APIs, CSS, React, UI design and more π https://t.co/K7u3kdRpQg
β Ines Montani γ°οΈ (@_inesmontani) June 27, 2019
In terms of actual *design* and building UIs, Refactoring UI is an amazing resource and it really helps pointing out those small little details that just "look good" but that you may have never noticed before. https://t.co/n1LHh8xMEb
β Ines Montani γ°οΈ (@_inesmontani) June 27, 2019
I think CSS becomes a lot more approachable once you really understand the rules and underlying concepts and focus on CSS layouts, rather than just styling. So the book I'll recommend is maybe a bit unusual: "Learn CSS Layout the pedantic way" https://t.co/nB1bBLBTDp
β Ines Montani γ°οΈ (@_inesmontani) June 27, 2019
We've switched over to FastAPI for our APIs: https://t.co/ImcEe37dwC
β Matthew Honnibal (@honnibal) June 26, 2019
I think you'll like it: it makes clever use of type annotations to reduce boiler-plate, and it generates Swagger docs.
π Handy guide to {flextable} for R Markdown!
β Mara Averick (@dataandme) June 26, 2019
π "flextable overview" by @DavidGohelhttps://t.co/YqXp8GHVMx #rstats #rmarkdown pic.twitter.com/Abqw2NlKai
Ever wondered how the Variational Autoencoder (VAE) model works? After reading this post, youβll be equipped with the theoretical understanding of the inner workings of VAE, as well as being able to implement one yourself. #AI #MLhttps://t.co/G9J6B87uyt
β Mariya Yao (@thinkmariya) June 26, 2019
A Visual Intro to NumPy and Data Representation
β Jay Alammar Ψ¬ΩΨ§Ψ― Ψ§ΩΨΉΩ Ψ§Ψ± (@jalammar) June 26, 2019
New blog post! A visual look at the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. We also look at how tables, images, and text are represented by numbers.https://t.co/fTnw1XfYy1 pic.twitter.com/WMLQA23fni
Also, you can *drag and drop your own data* if you want to use it for teaching! https://t.co/1ZspSj0G4W ππ Gotta find a way to work this into regression in the fall. Thanks, @Omar4ur!
β Amelia McNamara (@AmeliaMN) June 25, 2019
Motivation For Deep Learning https://t.co/eZ2bbpDzwV pic.twitter.com/rdB2dqZhj7
β Chris Albon (@chrisalbon) June 25, 2019
π Some super handy tipsβ¦
β Mara Averick (@dataandme) June 25, 2019
"Create Easy-to-Follow Technical Tutorials" π©βπ» @LittleMissData https://t.co/Gg9qBjaqk2 #rstats
Dynamic linear models with tfprobability: https://t.co/XVpaphazuO #rstats pic.twitter.com/aT5IufVUH5
β RStudio (@rstudio) June 25, 2019
