This chart by @ABSStats shows how laws and regulations regarding road safety actively save lives in Australia. pic.twitter.com/VokO2MthnV
— Simon Kuestenmacher (@simongerman600) November 17, 2020
This chart by @ABSStats shows how laws and regulations regarding road safety actively save lives in Australia. pic.twitter.com/VokO2MthnV
— Simon Kuestenmacher (@simongerman600) November 17, 2020
New post up at @PRRIpoll:
— Natalie Jackson (@nataliemj10) November 17, 2020
Hispanic opinion in the U.S. runs about 1/3 lean toward Republican views to 2/3 lean toward Democratic views.
What's the biggest dividing line besides party?
Religion. https://t.co/kDm3Ro0zR2 pic.twitter.com/FcQ2UKSX7J
This chart shows that Biden won in more educated states. Just another bit of data pointing to the shocking divide of the US. Source: https://t.co/HpEJgPduxb pic.twitter.com/MHlWXdqsxM
— Simon Kuestenmacher (@simongerman600) November 17, 2020
Internet ads are 100x less effective than 25yrs ago. On mobile devices, ≈50% of all clicks signal not customer interest but accidental "fat finger" clicks.
— Zachary Lipton (@zacharylipton) November 17, 2020
≈@timhwang
Denizens of ML Twitter, how much of your salary depends on subprime attention?https://t.co/f52jw6nE9i
This map, of the shift in Georgia from 2016 to 2020, is one of the most striking election maps I've ever seen, and know that I am old and have seen many mapshttps://t.co/an2x3fODvz pic.twitter.com/Lj5DoCWY9N
— Kevin Quealy (@KevinQ) November 17, 2020
The higher up you are, the further you can see. Makes sense. Chart by @neilrkaye shows the distance to the horizon wfor observers at different heights. Source: https://t.co/Af45gE4bTl pic.twitter.com/klvtnO7xwH
— Simon Kuestenmacher (@simongerman600) November 17, 2020
The unambiguously correct place to examine your training data is immediately before it feeds into the network. Take the raw x,y batch tuple, ship it back to CPU, unrender, visualize. V often catches bugs with data augmentation, label preprocessing, samplers, collation, etcetc.
— Andrej Karpathy (@karpathy) November 17, 2020
I believe that anyone writing network/web code will find their work more fun and easier if they deeply understand network protocols. And it's not that hard - they're plain text!
— Jeremy Howard (@jeremyphoward) November 17, 2020
This is a terrific series to start with, especially if you use Python.https://t.co/d8gSRdkr6c pic.twitter.com/P0PoYc2mKP
If you like ELECTRA, check out Electric, our new #emnlp2020 work that brought over energy-based model perspective ;) Electric produces fast pseudo-likelihood to improve speech recognition & translation reranking. Github: https://t.co/ErnTR4llRL & paper https://t.co/N2DzPJ3ksb pic.twitter.com/UfPYPnUDXS
— Thang Luong (@lmthang) November 16, 2020
Nice overview of the different tools (pandas, dask, rapids, datatable) and file formats (csv, feather, hdf5, jay, parquet, pickle) for working with larger file-based tabular datasets in Python https://t.co/Crma3vAOph
— Ben Hamner (@benhamner) November 16, 2020
The polls did NOT fail. Plot below shows @FiveThirtyEight's forecast plotted against the actual result. We do see an overall bias of about 3%. But this is not unusual and was accounted for. 92% of the confidence intervals covered and only GA, NC, & FL were in the wrong quadrant. pic.twitter.com/p8ikx0O9Zt
— Rafael Irizarry (@rafalab) November 16, 2020
must-read new study from @Google confirms all central claims of Deep Learning: A Critical Appraisal (2018):
— Gary Marcus (@GaryMarcus) November 16, 2020
- machine learning often generalizes poorly
- extrapolation beyond training data is key
- urgent need for better ways of adding in domain expertise https://t.co/gBygavHh7c