Every step of the visualization process changes with real data. Save yourself some grief and get the good stuff in the beginning. The Interactions Lab talks about their experiences https://t.co/hSnTRiBuoW
— Nathan Yau (@flowingdata) October 22, 2019
Every step of the visualization process changes with real data. Save yourself some grief and get the good stuff in the beginning. The Interactions Lab talks about their experiences https://t.co/hSnTRiBuoW
— Nathan Yau (@flowingdata) October 22, 2019
Americans support *beginning the impeachment process* by a 52.3%/42.9% margin. That's very close to Trump's approval rating split.
— Nate Silver (@NateSilver538) October 21, 2019
Support for impeachment per se is slightly lower, though. 48.2% support, 43.9% oppose. pic.twitter.com/ZXiaLn06ab
tidybayes killing it in the visualizing uncertainty department https://t.co/jMRy7buv6J pic.twitter.com/WiZ1doKKw4
— tj nightmahr 🎃🍕 (@tjmahr) October 21, 2019
there, you have them on one slide pic.twitter.com/DiEuULhtKB
— Thomas Wolf (@Thom_Wolf) October 21, 2019
Ok this is the one I'm going to end on.
— Sean J. Taylor (@seanjtaylor) October 19, 2019
- Cities are clearly ranked and listed in order.
- Divided it up into the important quantiles: 10th (shortest commutes), 25-75 (normal-ish range), and 90th (longest)
- Mean time is still visible (the dot), always higher than the median. pic.twitter.com/is7EZK9Ktp
Microsoft open sourced their visual data exploration tool SandDance https://t.co/L40EDpSvoZ pic.twitter.com/PCRdZvcmeR
— Nathan Yau (@flowingdata) October 18, 2019
Really really really liking this #dataviz article about how to identify fake news on Twitter. With the number of different charts to build up the story, and the level of creativity that borders on data art 👏👏👏 https://t.co/LFWheBi4NK pic.twitter.com/80LIok4FJU
— Nadieh Bremer (@NadiehBremer) October 17, 2019
A historical perspective on contextualizing your visualizations from @rkbrath https://t.co/vKVjuSeCqO
— Nathan Yau (@flowingdata) October 17, 2019
Captum – model interpret-ability & understanding library for PyTorch
— ML Review (@ml_review) October 17, 2019
Includes a web interface for easy visualization and access to a number of interpretability algorithms: Integrated Gradients, Saliency Maps, Smoothgrad, Vargrad and othershttps://t.co/QHPlf29n1v pic.twitter.com/bSmDpuZV2u
How commuting is too much? It depends on where you live. https://t.co/Zjsf3vlqwf pic.twitter.com/eIjAhzzxsS
— Nathan Yau (@flowingdata) October 16, 2019
"ExBert - A Visual Analysis Tool to Explore Learned Representations in Transformers Models" -- this looks like a nice one! Tool: https://t.co/YZqHvUBD5h and paper: https://t.co/NEJG4LKGzP pic.twitter.com/q96JWrWfD2
— Sebastian Raschka (@rasbt) October 15, 2019
Woohoo! SUPER happy to have my @GoogleNewsLab collaboration about "How people search to better understand their 🐱 or 🐶" in the @infobeautyaward shortlist! 🎉
— Nadieh Bremer (@NadiehBremer) October 15, 2019
I would be honored (and really really happy) to receive your vote 😃https://t.co/VR4xc5FpVC
🙏🙏🙏 pic.twitter.com/yGyOnNSV5c