Google just released 2 new NLP dialog datasets collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user': https://t.co/cNLTaTQ4gQ
— Peter Skomoroch (@peteskomoroch) September 6, 2019
Trained a ResNet-50 that can recognise shot scale in images (Close Up, Long, Medium, etc) with a custom dataset. Fascinating heatmaps!
— Rahul Somani (@rsomani95) September 6, 2019
Thanks to @math_rachel @jeremyphoward and @GuggerSylvain for the fastai library
Project: https://t.co/wgcA1TTJ7g
Code: https://t.co/iecxHlfKmr pic.twitter.com/jabs0VY8TX
Insider Trading Visualized With Python https://t.co/XYgNWOitn3
— PyCoder’s Weekly (@pycoders) August 31, 2019
If you use Twitter data for research and are involved in academic use of the data, you may want to pay attention to some upcoming changes that Twitter is planning to make that may cripple the ability to get data from Twitter's API.
— Jason Baumgartner (@jasonbaumgartne) August 28, 2019
Follow the thread here. This looks bad. https://t.co/kNNGzYiArU
Waymo open-sourced their autonomous driving dataset from its self-driving fleet.
— Reza Zadeh (@Reza_Zadeh) August 26, 2019
- 1TB over 1000 segments, collected at 10Hz
- 12M 3D bounding box labels with tracking IDs
- 5 lidars: 1 mid-range (top) & 4 short-range lidars (sides)
- Diverse conditionshttps://t.co/tpUXfy8dtJ pic.twitter.com/kIZlCi19EC
I have been collecting all tweets made by @realdonaldtrump, all tweets sent by Trump and all tweets that mention his account. This will be a complete archive of all activity revolving around his account since 2015.
— Jason Baumgartner (@jasonbaumgartne) August 20, 2019
This data set will contain over one billion tweets and allow... pic.twitter.com/IMpA6afYwQ
For anyone who's under the impression that working on machine learning projects is just training & tuning models, I'd recommend this blog post about how hard it can be to just get your hands on data in the first place! https://t.co/nGCj9xfvuC
— Rachael Tatman (@rctatman) August 15, 2019
“algorithms—and content moderators who grade the test data that teaches these algorithms how to do their job—don’t usually know the context of the comments they’re reviewing”
— Angela Bassa (@AngeBassa) August 15, 2019
Then what even is the point of the labeling exercise?! https://t.co/G7qtiMJJqV
We just open sourced LVIS, a dataset with high quality instance segmentations! v0.5 contains 1230 object categories in 82k images annotated with more than 700k segmentation masks for training & validation. Learn about it and the upcoming @ICCV19 challenge: https://t.co/fVpwavqosp
— Facebook AI (@facebookai) August 8, 2019
We've analyzed the results of the Dask User Survey: https://t.co/GOaSHF8COE
— Dask (@dask_dev) August 6, 2019
Or run your own analysis on @mybinderteam: https://t.co/Xn1rm3KgsD
Thanks to everyone who participated!
Available at: https://t.co/Gu8hKIvp2l https://t.co/Rv480943QA
— Stanford NLP Group (@stanfordnlp) July 31, 2019
The 1.2 million coordinates referenced in the English #Wikipedia. #dataviz
— Randy Olson (@randal_olson) July 29, 2019
Full res: https://t.co/5yryei3i6z
Source: https://t.co/hEwuYBcmMN pic.twitter.com/vwIq8WI99n
