Sometimes it pays off to go against the trend — even if it fails.
— hardmaru (@hardmaru) May 28, 2019
“Some might judge the team harshly but the project simply explored an underexplored space — which was off limits because of prejudice.”
“This is what we are supposed to do as scientists.”https://t.co/GNP38BROOq
I analyzed 180M tweets from 13 metropolitan areas to see what emojis are uniquely popular in each city. Of course Bay Area is winning with rockclimbing and scooter, DC with Pinocchio, NYC with no pedestrian, and Toronto with Birkenstock. pic.twitter.com/9DlqchgM7b
— Chip Huyen (@chipro) May 27, 2019
Build a Hardware-Based Face Recognition System for $150 With the Nvidia Jetson Nano and Python https://t.co/RbIf4soI5h
— PyCoder’s Weekly (@pycoders) May 26, 2019
The new citations tracker on arXiv is an abomination, compromising a public utility and dragging it into the swirling vortex of bad academic patterns. It's like projecting ads onto water as it pours out of the faucet. Please kill this! @arxiv_org pic.twitter.com/LUGaEoQf5y
— Zachary Lipton (@zacharylipton) May 26, 2019
. @github has a new metric for software repositories: number of times the tool is used.@scikit_learn is used by 55,634 other repositories@TensorFlow by 39,969
— Gael Varoquaux (@GaelVaroquaux) May 25, 2019
numpy, by 205,143 (woot!)... pic.twitter.com/v2ObshLBgz
New blog post! "My First Year at UW-Madison and a Gallery of Awesome Student Projects" -- a gallery of awesome Machine and Deep Learning projects our @UWMadisonSTATS undergrads were working on: https://t.co/xEetakYddv pic.twitter.com/wztdqaReFX
— Sebastian Raschka (@rasbt) May 25, 2019
Good advice for those pursuing a career in machine learning research https://t.co/WnqWqZTkWk pic.twitter.com/LQ9FKxjz9m
— hardmaru (@hardmaru) May 24, 2019
New story: Google has made a big push to make the YouTube homepage and app a destination. Pitched advertisers on a tenfold increase in watch time there.
— Mark Bergen (@mhbergen) May 23, 2019
One factor behind that rise is the "trashy video classifier." https://t.co/JRslSPAkWm
Henry Minsky: What are some fundamental problems in Computer Vision that Deep Learning may not be able to solve? https://t.co/DKoSKr7BNO
— Gary Marcus (@GaryMarcus) May 22, 2019
Excited to share some of the work I've been doing at @NvidiaAI on @rapidsai, working with @cmgreen210 and @nkoumchatzky on deep learning for tabular data. We're just getting started down this road, but we've taken the first step. Check out the blog: https://t.co/cM1fnb4Tzs
— Even Oldridge (@Even_Oldridge) May 22, 2019
super interesting article on synthetic data and how it's being applied across industries: https://t.co/ewZ3dOscv5
— Josh Tobin (@josh_tobin_) May 22, 2019
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead | Nature Machine Intelligence https://t.co/JTmVgGowjB
— Edward Tufte (@EdwardTufte) May 21, 2019
