📽 slides from my talk at @noreastrconf…
— Mara Averick (@dataandme) October 27, 2018
❓"How I found your answer" https://t.co/wFnEpCZNVL
(also on SpeakerDeck https://t.co/bWs2qlIeoE) pic.twitter.com/xiLC3OtQdD
📽 slides from my talk at @noreastrconf…
— Mara Averick (@dataandme) October 27, 2018
❓"How I found your answer" https://t.co/wFnEpCZNVL
(also on SpeakerDeck https://t.co/bWs2qlIeoE) pic.twitter.com/xiLC3OtQdD
The Five Major Objectives of the M4 Forecasting Conference:
— Spyros Makridakis (@spyrosmakrid) October 27, 2018
New York CIty, December 10 and 11 https://t.co/dC6vpmRjBC
For information about the comparisons of ML and Stat forecasting methods for the M3 Competition https://t.co/S3BpRgtxUW
for the M4 https://t.co/fYp0Mn2fCn pic.twitter.com/I74LnHOOUI
If you have a problem with your #rstats code, use the reprex 📦 to make a minimal reproducible example! Will help others help you, and you may even figure out the bug yourself. Learn more here: https://t.co/2RiORBnQ9C @dataandme #noreastr18 pic.twitter.com/3ZiVorFUAz
— Emily Robinson (@robinson_es) October 26, 2018
Playing Mortal Kombat with TensorFlow.js https://t.co/taRHHqblOC
— François Chollet (@fchollet) October 25, 2018
Training ML models takes time and losing your weight values can happen at the touch of CTRL-C. In this #CodingTensorFlow @MagnusHyttsten shows you how to load and save models at every epoch so you never lose time or data.
— TensorFlow (@TensorFlow) October 25, 2018
Watch here → https://t.co/LKrQBKSNhY pic.twitter.com/Ihum7i4hxG
And the slides are available at https://t.co/nWafl9SvuG https://t.co/DORWnmd6Eq
— Adam Paszke (@apaszke) October 25, 2018
😱 don't know how I slept on this one, but new 📕 from @rdpeng
— Mara Averick (@dataandme) October 25, 2018
"Putting It All Together…" 👨🏻💻 Roger D. Peng https://t.co/f42XRRBfIy ht @flowingdata pic.twitter.com/6UhBs9eM0w
Tutorials for #DataScientists — #DataScience Simplified in 11 parts: Regression, Classification, Model Selection, Validation, Generalization, Overfitting, Bias-Variance trade-off, Confusion Matrix,.. https://t.co/0xXTaum0aY #abdsc #BigData #MachineLearning #Statistics #Algorithms pic.twitter.com/gUAkboFFh5
— Kirk Borne (@KirkDBorne) October 25, 2018
Several updates have been made to the Introduction to Data Science online book. The main one being the addition of dozens of exercises to the wrangling, regression and machine learning sections.
— Rafael Irizarry (@rafalab) October 24, 2018
A PDF version is coming soon.https://t.co/kgqd7wV611
new blog post: understanding multinomial regression with partial dependence plots https://t.co/8lXH2HBNR1
— alex hayes (@alexpghayes) October 24, 2018
targeted at #rstats users starting to explore non-linear models! pic.twitter.com/kTbbklngQB
Starting to learn about CNNs and looking for fun projects to practice? 😉Or maybe curious about @kaggle or interested in checking out @fastdotai v1?
— Radek Osmulski (@radekosmulski) October 23, 2018
Please find the Quick, Draw! competition starter pack with instructions on how to get started here https://t.co/kOtu2klAuk
👍 write-up and case study…
— Mara Averick (@dataandme) October 22, 2018
"Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements" ✍️ @f2harrellhttps://t.co/OnPCZx3hI4 #rstats pic.twitter.com/MiwINeBYAR