Day 40: Plotly 3.0.0

People make "bad" charts all of the time, and it's not just to lie. Sometimes bad is good. Sometimes bad is an honest mistake. Sometimes bad is learning. https://t.co/KYlUQFAdf9

— Nathan Yau (@flowingdata) June 28, 2018

Really cool paper by @JonathanBerant and student on efficient QA over long documents represented as trees. Great idea and very well written!https://t.co/7gJ9iyZFxZ#NLProc

— Victor Zhong (@hllo_wrld) June 28, 2018

#BigPandas https://t.co/iaTrvJ1TBC

— Chris Albon (@chrisalbon) June 28, 2018

The DoD has spent a lot of money at Trump's hotels since he was elected. Explore this excellent dataset from @ProPublica on payments to Trump properties before and after the election. https://t.co/5unjQSKJzd

via @axios @AxiosVisuals pic.twitter.com/zRaFuDi0nN

— Harry Stevens (@Harry_Stevens) June 28, 2018

The Illustrated Transformerhttps://t.co/t8neQG650S

New blog post visually explaining the model from "Attention is All You Need". It achieved state-of-the-art in some machine translation tasks in 2017. It is also the reference model for coding for Google's Cloud TPUs. pic.twitter.com/PjeCyKwZRU

— Jay Alammar جهاد العمار (@jalammar) June 27, 2018

@observablehq notebook on backpropagation and automatic differentiation. Just 20 lines of javascript - without AI framework. https://t.co/TAxFUpWePa

— Steven De Keninck (@enkimute) June 28, 2018

First figure from our paper: how the LSTM with a twist allows for the equivalent speed of a plain convnet by running efficiently in parallel on GPUs, like image processing convents.https://t.co/2PpYOkyBSn pic.twitter.com/gubGw92NT3

— Smerity (@Smerity) June 28, 2018

💖 great read:
“Explaining the Gap: Visualizing One’s Predictions ⇧ Recall & Comprehension of Data” by @uwdata https://t.co/r39V61uSJd #dataviz #infovis pic.twitter.com/vVq9mFnYkJ

— Mara Averick (@dataandme) June 29, 2018

Just discovered Terence Parr's website, amazing content.

I'm working my way through "The Matrix Calculus You Need For #DeepLearning" awesome stuff, be sure to check it out.

Big thanks to @the_antlr_guy for the article!https://t.co/e3BejrI17U pic.twitter.com/UA9q1EoNA7

— DataOptimal (@DataOptimal) June 28, 2018

The rewrite of the Advanced R functions chapter is now complete: https://t.co/PqNwrMw7sh. Please take a look! #rstats

— Hadley Wickham (@hadleywickham) June 28, 2018

Multiverse ✕ tidyverse ⇨ 💥
"A tidy text analysis of Rick and Morty" 👨‍🚀 @tudosgarhttps://t.co/siXDEvYZOb #rstats #tidytext #textmining pic.twitter.com/NDC6qZIHLc

— Mara Averick (@dataandme) June 28, 2018

🌟 Announcing https://t.co/QXafzjjSCp 3.0.0 🌟
1 year in the making, this is our biggest release yet:
- Deep Jupyter widgets integration
- Interactive plotting of >1M points in Jupyter 🚀
- 1st-class JupyterLab support
Plus much more! Read how we did it: https://t.co/yQRuj3J5iL pic.twitter.com/RjqV5jwmiS

— plotly (@plotlygraphs) June 28, 2018

SNIPER - An efficient multi-scale object detection algorithm. https://t.co/cB1ByIWZym #python #algorithm pic.twitter.com/hK4MvtJqHc

— Python Weekly (@PythonWeekly) June 28, 2018

A live Graphviz editor for all your directed and undirected graph needs. https://t.co/EjYG0PdYe9

— Mike Bostock (@mbostock) June 29, 2018

I know it's a widely disliked position of mine, but if the substantive conclusions of your research depend on software, you unfortunately need to take personal responsibility for the software you use: https://t.co/vw2CbnRD5l

— John Myles White (@johnmyleswhite) June 28, 2018

I talk about this here! https://t.co/saWOcyTPjM #rstats https://t.co/1mfZInX6OZ

— David Robinson (@drob) June 28, 2018

Can machines even see my queens as I view them? Can machines ever see our grandmothers as we knew them? - humans worthy of respect, https://t.co/CLcGD6674L

— Joy Buolamwini (@jovialjoy) June 28, 2018

Today in office hours a student came by with a Python bug that essentially boiled down to this:

def g(x):
def f():
x += 1
f()
g(0)

That was a fun one to explain 😀

— Jake VanderPlas (@jakevdp) June 28, 2018

The bare truth of science:
1. Nobody believes a computational model except the person who built it. 2. Everybody believes an experimental measurement except the person who made it.

— Ash Jogalekar (@curiouswavefn) June 28, 2018