Today Roger Federer won his 100th ATP tournament — the first player ever to do so.
— John Burn-Murdoch (@jburnmurdoch) March 2, 2019
I made a chart showing his peerless progress, and I’ve published the full step-by-step process for going from the #rstats #ggplot2 defaults to the end product https://t.co/6F0Bcqdmk3 #dataviz pic.twitter.com/UmrwjmM1hY
Mastering Fast Gradient Boosting on Google Colaboratory with free GPU
— ML Review (@ml_review) February 28, 2019
By @besab @CatBoostML
Many think GBDT cannot be efficiently accelerated by a GPU, but this is actually not true.
Yandex CatBoost runs 4x faster on Tesla K80 vs Intel Xeon E5–2600v3 https://t.co/TJ5gg9tkld pic.twitter.com/PTEdlMBnHn
"AdaBound: An optimizer that trains as fast as Adam and as good as SGD" -- looks like it'll be worth checking out! (also seems to come with a PyTorch implementation :)) https://t.co/lGUtUwt3U2 pic.twitter.com/s5suoVgCbY
— Sebastian Raschka (@rasbt) February 26, 2019
Reproducible Benchmarking of Keras and PyTorch Models https://t.co/Lmksa6nKPF #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #keras #pytorch
— PyTorch Best Practices (@PyTorchPractice) February 22, 2019
PT-BERT 0.5 out💥
— Thomas Wolf (@Thom_Wolf) February 11, 2019
Pretty big release w. not 1 but TWO new pretrained models:
-classic: OpenAI's GPT
-brand-new: Transformer-XL by Google/CMU
As always both should be super easy to use
So...BERT now stands for Big-&-Extending-Repository-of-Transformers😅
Happy Transfer Learning! pic.twitter.com/yIdTHHJKKA
PyTorch implementation of "Super-Realtime Facial Landmark Detection and Shape Fitting by Deep Regression of Shape Model Parameters" https://t.co/kAFzsIElJx #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) February 10, 2019
FAIR has released code for the robust ImageNet model by Cihang Xie et al: https://t.co/yB53YRJIUP
— Ian Goodfellow (@goodfellow_ian) February 8, 2019
NVIDIA's StyleGAN implementation just open-sourced. Very cool stuff, please use responsibly! https://t.co/9P83Ohzf1d
— Eric Jang (@ericjang11) February 6, 2019
This article, attached notebook, and audio library, (from @johnhartquist) are really really great. Probably the most thoughtful and complete fastai integration I've seen outside the official library.
— Jeremy Howard (@jeremyphoward) February 5, 2019
And apparently he's looking for a job... :) pic.twitter.com/Sg7vx15qo1
PyTorch Implementation of XNOR-Net https://t.co/NyQ95DvPtR #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) February 3, 2019
This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.https://t.co/lTAGE7LGZY pic.twitter.com/wfSyTplBR3
— Sebastian Ruder (@seb_ruder) February 1, 2019
Have you ever wanted to see seven different Bayesian and frequentist ways to measure the avg difference between two groups? Check out my new post to see examples of standard #rstats t-tests, the infer package, brms regression, and raw @mcmc_stan code! https://t.co/U2sjL9nt6b pic.twitter.com/OgK1zsQXiY
— Andrew Heiss, PhD (@andrewheiss) January 29, 2019
