FairSeq Toolkit
FairSeq Toolkit - Major Update
— PyTorch (@PyTorch) June 16, 2018
- Distributed Training
- Transformer models (big Transformer on WMT Eng-German in < 5 hours on DGX-1)
- Fast Inference: translations @ 92 sent/sec for big Transformer
- Story Generation
Read more at Michael Auli's post: https://t.co/eptKDuh0WI pic.twitter.com/d4OtJZpdFw
code and pre-trained models to reproduce the recent paper "Scaling Neural Machine Translation" (https://t.co/mrRDmlwax1) where we train on up to 128 GPUs with half precision floating point operations as well as delayed batching.
— PyTorch (@PyTorch) June 16, 2018
Create a ChatBot with tf-seq2seq
How To Create a ChatBot With tf-seq2seq For Free! – Deep Learning as I See It https://t.co/3UuHNI2gwV #AI #DeepLearning #MachineLearning #DataScience
— Mike Tamir, PhD (@MikeTamir) June 16, 2018
Visualization
Comparing values is a fundamental and critical task in any data analysis. And yet, so many subtleties! A visual meta-comparison. https://t.co/uDbeNCkNeZ
— Mike Bostock (@mbostock) June 15, 2018
These four maps show the same exact data, but look wildly different. What the? Here’s why: https://t.co/uDbeNCkNeZ pic.twitter.com/0vPQSg7qBw
— Mike Bostock (@mbostock) June 15, 2018
Outflow of #refugees from #Syria to other countries in 2016. #datavizhttps://t.co/n6TAWDdeFj pic.twitter.com/QcEATAGyuG
— Randy Olson (@randal_olson) June 15, 2018
Notable Research
While there are many attempts to map Auto ML to deep learning, to automate the tedious hyperparam tuning, here's a smart alt. via good initialization schemes: "Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla ConvNets" https://t.co/zt9o89uP0K
— Sebastian Raschka (@rasbt) June 15, 2018
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
— Andrew Gordon Wilson (@andrewgwils) June 15, 2018
Our new paper (+code!): https://t.co/5dRLFqgJlh
By analyzing loss geometry, we achieve record semi-supervised results, including 95% accuracy on CIFAR-10 with only 4000 labels! pic.twitter.com/EfqDhr3qC3
Text to speech with few data. https://t.co/JiX6TglXAw
— Nando de Freitas (@NandoDF) June 15, 2018
Autoregressive Quantile Networks for Generative Modeling: https://t.co/MImdcFiYxj
— DeepMind (@DeepMindAI) June 15, 2018
Tutorials and Resources
This week's #KernelAwards winner uses the Stack Overflow 2018 Developer Survey to better understand what kinds of users are likely to identify as part of the Stack Overflow community: https://t.co/BMcvm7Pv2B pic.twitter.com/SGbu5BhLNu
— Kaggle (@kaggle) June 15, 2018
NCRF++ : A neural CRF++ toolkit for sequence labeling tasks. Works pretty much similar to Taku's CRF++ package, but built with #PyTorch! #nlproc https://t.co/05ZL6bEcoO pic.twitter.com/ME8k7ruMdA
— Delip Rao (@deliprao) June 16, 2018
GitLab's Web IDE looks pretty nice. Sooner or later entire workflows will take place in browser tabs. https://t.co/SBrBMfJb0x pic.twitter.com/Ts6OHuDVyk
— hardmaru (@hardmaru) June 15, 2018
Libraries like Vega-Lite and Vega, that are built on top of #D3js, really deserve to be better known. Unless the visualization you want to build is really novel, you'll be much better served by starting with those! @vega_vis #DataScience #dataviz https://t.co/isSS5eYJxk
— Christian Hudon (@christian_hudon) June 14, 2018
Python
Clean architectures in Python: a step-by-step example – https://t.co/sxye7fOSuh
— Pycoders Weekly (@pycoders) June 15, 2018
rstats
Save time with RStudio code snippets! See how they work -- and how to write your own -- in my latest "Do More With R" #rstats screencast: https://t.co/WtnqPsOKkE pic.twitter.com/bBW5wrmZzc
— Sharon Machlis (@sharon000) June 15, 2018
🌟 guide by @mjfrigaard:
— Mara Averick (@dataandme) June 15, 2018
"Getting started w/ stringr for textual analysis in R"https://t.co/ZX27YWn6BQ via @storybench #rstats #stringr pic.twitter.com/7834zO817y
new blog post: a casual case study on how to speed up #rstats code featuring xml2, rcpp and furrrhttps://t.co/KOACH1O8dy pic.twitter.com/63T0SMsxH7
— alex hayes (@alexpghayes) June 15, 2018
ICYMI, 😱 inputs, interfaces & more:
— Mara Averick (@dataandme) June 16, 2018
"The R Shiny 📦s you need for your web apps" by @AntGuilllot https://t.co/gtTZRGjr0v #rstats #rshiny pic.twitter.com/5fLF62qAJZ
🌟 guide by @mjfrigaard:
— Mara Averick (@dataandme) June 15, 2018
"Getting started w/ stringr for textual analysis in R"https://t.co/ZX27YWn6BQ via @storybench #rstats #stringr pic.twitter.com/7834zO817y
My #xaringan slides on how use #xaringan to make slides with #rstats: https://t.co/4lZudRoGBn Raw Rmd here: https://t.co/QeFEdYlaGv pic.twitter.com/7QOUqawFQf
— Dr. Alison Hill (@apreshill) June 13, 2018
Miscellaneous
We haven't yet solved even 10% of the problems we could solve with existing AI/ML techniques. Even if new research were to deliver nothing from now on, there still wouldn't be another AI winter. AI/ML will keep on delivering for years to come.
— François Chollet (@fchollet) June 16, 2018
Fortunately, this should improve the productivity and focus of our community, rather than harm it. Our biggest successes are still ahead
— François Chollet (@fchollet) June 16, 2018
—Deliberately design end-to-end workflows.
— 👩💻 DynamicWebPaige @ GOTO; Amsterdam 🌷🇳🇱 (@DynamicWebPaige) June 15, 2018
—Reduce cognitive load for your users.
—Provide helpful feedback to your users.
In the long run, good design always wins, because it makes its adepts more productive and impactful.
Good design is infectious. ✨https://t.co/KdG7IT7Kwh pic.twitter.com/bXKkQpqJLP
Something you develop the longer you do data analysis is a "spidey sense" when something "looks wrong" that can often lead to uncovering a problem with your dataset or analysis. pic.twitter.com/7EwcXiVoyu
— Data Science Renee (@BecomingDataSci) June 15, 2018