One of the underappreciated strengths of the Julia creators is Jeff Bezanson's invariable honesty, seen clearly in his talk "What's Bad about Julia?": https://t.co/AsqDrsumB3
— John Myles White (@johnmyleswhite) July 26, 2019
One of the underappreciated strengths of the Julia creators is Jeff Bezanson's invariable honesty, seen clearly in his talk "What's Bad about Julia?": https://t.co/AsqDrsumB3
— John Myles White (@johnmyleswhite) July 26, 2019
📺 Powerhouse panel on privacy-preserving #AI, federated learning, and encryption recorded live at #RAAIS2019: @iamtrask of @openminedorg, Brendan McMahan of @GoogleAI, Peter Eckersley of @PartnershipAI and @mortendahlcs of @dropoutlabsai.
— Nathan Benaich (@NathanBenaich) July 25, 2019
Watch here👉 https://t.co/EHalTkQM8q
Watch "Visualizing uncertainty with hypothetical outcomes plots" by @ClausWilke Claus Wilke
— RStudio (@rstudio) July 25, 2019
from rstudio::conf(2019)
🎦 https://t.co/XwqGeNSXAj
Learn more about rstudio::conf(2020) in San Francisco: https://t.co/BGi7aYWwty #rstats #DataScience pic.twitter.com/agzCu9DFyv
Just gave a talk on my recent line of work on ***Robust Deep Learning Under Distribution Shift*** at @SimonsInstitute. Topics incl. impossibilities, label shift, detection, importance weighting & domain-adversarial methods https://t.co/JrOLiV0jXX
— Zachary Lipton (@zacharylipton) July 18, 2019
Take a coffee break with @dan_s_becker and @rctatman as they discuss probabilistic programming, why Dan thought this whole machine learning thing would never pan out... and college pranks gone wrong. 😝 [WATCH] https://t.co/h1yj7i8DoE
— Kaggle (@kaggle) July 16, 2019
My #SciPy2019 keynote: The New Era of NLPhttps://t.co/7ELrqzoRpZ pic.twitter.com/PVYXN0piCP
— Rachel Thomas (@math_rachel) July 16, 2019
See below for 12 fantastic talks about NLP research and applications featuring @yoavgo, @MarkNeumannnn, @OxyKodit, me, and many more! https://t.co/5UdbeD0E2G
— Sebastian Ruder (@seb_ruder) July 15, 2019
Really great keynote by @math_rachel on recent developments in NLP and ML, the principles of transfer learning and the dangers of algorithms that create realistic prose and videos https://t.co/RzzsXD5V9s
— Andreas Mueller (@amuellerml) July 12, 2019
Introduction to Algorithmic Bias: covers types of basis, debunks common myths about bias, and shares steps towards addressing biashttps://t.co/jv53akGMfI pic.twitter.com/8CLVmTG3Co
— Rachel Thomas (@math_rachel) July 8, 2019
An outstanding keynote by @dhh redefining what free software can mean.
— Radek Osmulski (@radekosmulski) July 3, 2019
So many important subjects discussed along the way (self-actualization, role of science in programming, burnout)
A dose of humanity that feels like a breath of fresh airhttps://t.co/JyaMEz6p7A
☕️☕️ time with @rctatman! Today she chats with @felipehoffa, a database expert working on Google BigQuery. They talk about optimizing, common misconceptions about databases and some cool datasets that will be released soon. [WATCH] https://t.co/Vc5xiPImw1
— Kaggle (@kaggle) July 1, 2019
Thanks @tousifsays for pointing me to this wonderful course that addresses fundamental theories often ignored in regular AI classes, including Random Matrix Theory, Sparse Coding, Harmonic Analysis.
— Chip Huyen (@chipro) June 24, 2019
Videos: https://t.co/UHA2mWAf8A
Lecture notes: https://t.co/fY4utjOp5b