Dask has a Youtube channel filled with quick 5-10 minute screencasts.https://t.co/tHi0VDJzQv?
— Dask (@dask_dev) October 29, 2019
Dask has a Youtube channel filled with quick 5-10 minute screencasts.https://t.co/tHi0VDJzQv?
— Dask (@dask_dev) October 29, 2019
pyhttptest - A command-line tool for HTTP tests over RESTful APIs. https://t.co/g9I6JXTbAl #python #rest #api
— Python Weekly (@PythonWeekly) October 28, 2019
The @rapidsai 0.10 release is out! Numerous core perf improvements, t-SNE over 1000x @scikit_learn, & contributions to @datashader @dask_dev @XGBoostProject & more. Decades of #OpenSource inspires RAPIDS to bridge the #gpu #DataScience community. Join in! https://t.co/p1YyTCTEfW
— RAPIDS AI (@rapidsai) October 28, 2019
Microsoft Researchers with collaborators at @CarnegieMellon and @Stanford created PipeDream, a new way to parallelize deep neural network training. See how PipeDream gets 5.3x faster training time by combining intra- and inter-batch parallelism: https://t.co/8ieraUBKVr #SOSP19
— Microsoft Research (@MSFTResearch) October 28, 2019
instaloader - Download pictures (or videos) along with their captions and other metadata from Instagram. https://t.co/jUpxdbsAWY
— Python Trending (@pythontrending) October 27, 2019
A new research language has been open-sourced from the #JAX team at @GoogleAI! 👩💻✨
— 👩💻 DynamicWebPaige @ #TFWorld 🌍 (@DynamicWebPaige) October 26, 2019
Comments:
"I like the syntax. Being able to define a lambda with
f x y = x + y
or define a table with
z.i = x.i + y.i
looks really clean."
"I really want a language like this to go mainstream." https://t.co/HYMl0fgTh5
Does #deeplearning "understand" natural language? Has common sense been solved? Try for yourself at https://t.co/8LkSUMTrDv, from @AdamDanielKing.
— Gary Marcus (@GaryMarcus) October 26, 2019
Here's a sample from this morning, my question in bold, transformer network answer at bottom, starts "It's the same as... " pic.twitter.com/SMd8VDLh0h
Thanks to @ptrblck_de we have fast fp16 depthwise convs in @PyTorch - and he even created a create benchmark spreadsheet to show where they help.
— Jeremy Howard (@jeremyphoward) October 26, 2019
However, that spreadsheet is a bit tricky to interpret, so I've made a couple of summary tables below.
1/3https://t.co/Uy5SYs1VwD
Excellent work on a flexible framework for handling nonstandard interpretations in probabilistic programming. For example, different inference strategies to estimate integrals in a composable manner. Was a nice read! https://t.co/OT8b5iqeph
— Dustin Tran (@dustinvtran) October 25, 2019
TensorSketch! https://t.co/GnryzBPctc
— hardmaru 😷 (@hardmaru) October 25, 2019
Netflix open-sources Polynote to simplify data science and machine via @rightrelevance thanks @sallyeaves https://t.co/pQRZzJUCEb
— Bojan Tunguz (@tunguz) October 24, 2019
EagerPy is a thin wrapper around PyTorch and TensorFlow Eager that unifies their interface and thus allows writing code that works with both. https://t.co/ogUjDgR7T3 #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #tensorflow
— TensorFlow Best Practices (@TFBestPractices) October 24, 2019