PyWaffle: Make Waffle Charts in Python https://t.co/8WczIQWuj5
— PyCoder’s Weekly (@pycoders) October 14, 2019
PyWaffle: Make Waffle Charts in Python https://t.co/8WczIQWuj5
— PyCoder’s Weekly (@pycoders) October 14, 2019
PyTorch 1.3 includes support for model deployment to mobile devices, quantization, & front-end improvements, like the ability to name tensors. New tools & libraries are also launching for improved model interpretability & multimodal development. Read more: https://t.co/HaaIt9cU3U pic.twitter.com/UKHdU7sHIH
— Facebook AI (@facebookai) October 10, 2019
https://t.co/tbNxduCZNp is something was launched for researchers but it will be a game changer for applied AI. They run models inference on their GPUs so you get a trusted info about:
— Piotr Czapla (@PiotrCzapla) October 10, 2019
- Inference time on V100
- The performance vs paper
- How hard is to run the model pic.twitter.com/gzl99VMlHV
sotabench 🏅🛠—This is a great new service by @paperswithcode!
— Sebastian Ruder (@seb_ruder) October 10, 2019
It enables benchmarking open-source ML models and comparing the results to what was reported in the paper—directly from GitHub repos. Should be a step towards more replicability in ML!https://t.co/5diJXFTFxX pic.twitter.com/j1jn1Tdljt
Here are our slides from yesterday's #MetaForum2019 in Brussels 🇪🇺 https://t.co/VR57aoQG5K
— Ines Montani 〰️ (@_inesmontani) October 10, 2019
As promised, the first tutorial showing how to use the fastai.medical.imaging library is now available.
— Jeremy Howard (@jeremyphoward) October 9, 2019
It covers analyzing DICOM metadata with a Pandas extension method; combining metadata, pixel stats, & labels; working with data types; viewing; & morehttps://t.co/CqsLkojMSx pic.twitter.com/Zzot76l3uY
Announcing TorchBeast, an IMPALA-inspired @pytorch platform for distributed RL research. Used in a growing number of projects here at @facebookai. Project lead by Heinrich Küttler, with major effort by @nntsn et al.
— Edward Grefenstette (@egrefen) October 9, 2019
Paper: https://t.co/HMYTtesSqC
Code: https://t.co/vadpUsyvaZ
Is your medical image computing software this helpful?
— Jeremy Howard (@jeremyphoward) October 9, 2019
The fastai.medical.imaging library is! :)
I'll be posting tomorrow a little tutorial on how to use the pre-release version with the RSNA Kaggle competition. pic.twitter.com/lg4sppA6UF
Dask partners well with Numba. Numba compiles code to run well on one machine, and Dask scales that code out to many machines.
— Dask (@dask_dev) October 7, 2019
Here is a quick example showing smooth integration with the two libraries.https://t.co/XKT3PcTcWO
👂 Sonify a visualization…
— Mara Averick (@dataandme) October 7, 2019
🔊 "Introducing {devoutaudio} - an experimental package for rendering graphics as audio" by @coolbutuselesshttps://t.co/jY524Mzy1u #rstats pic.twitter.com/h5lHVHiOwR
hydra - Hydra is a framework for elegantly configuring complex applications https://t.co/SnUdO3JTey
— Python Trending (@pythontrending) October 5, 2019
pandas-profiling: Create HTML Profiling Reports From Pandas DataFrame Objects https://t.co/j75jFZB8BN
— PyCoder’s Weekly (@pycoders) October 5, 2019