Here's a great overview from @TeachTheMachine of our Numerical Linear Algebra course (taught by @math_rachel)https://t.co/KhrTp7hg05
— Jeremy Howard (@jeremyphoward) September 17, 2018
Here's a great overview from @TeachTheMachine of our Numerical Linear Algebra course (taught by @math_rachel)https://t.co/KhrTp7hg05
— Jeremy Howard (@jeremyphoward) September 17, 2018
[free 28-page eBook] #Probability and #Statistics Cookbook for #DataScientists (and for other scientists) https://t.co/RwEIVaeZte #abdsc #BigData #Analytics #DataScience #MachineLearning #Mathematics #StatisticalLiteracy #DataLiteracy
— Kirk Borne (@KirkDBorne) September 17, 2018
(attached graphic shows a sample) pic.twitter.com/BGECgWVolt
🤓 dig the visuals on these…
— Mara Averick (@dataandme) September 16, 2018
"Stanford CS 229 machine learning cheatsheets" 👬 @afshinea & @shervineahttps://t.co/VKV2UGkWzN #MachineLearning #statistics pic.twitter.com/F0x5Iyqyoi
This paper just came to my attention (and I can't forgive myself for not seeing it earlier) https://t.co/BHbKcaWotl
— Judea Pearl (@yudapearl) September 16, 2018
A friendly, comprehensive and unifying (no hangups) roadmap to epidemiological methodology. Highly recommended. #Bookofwhy#causalinference @EpiEllie @miquelporta
The process of data analysis proceeds through different phases that involve both divergent and convergent thinking. This can be a useful mental model for describing various phenomena in data science and for highlighting areas for future work. https://t.co/Uo5WgUV7tG pic.twitter.com/bJ8g5ZOqSq
— Roger D. Peng (@rdpeng) September 14, 2018
My keynote about the future of natural language process, #deeplearning, multitask learning and https://t.co/NV7H5FQaer
— Richard (@RichardSocher) September 14, 2018
For the remote conference
AI with the besthttps://t.co/AhF6HMcnlA
Two #Statistics articles for #DataScientists:
— Kirk Borne (@KirkDBorne) September 13, 2018
1) Statistical Tests — When to use Which? https://t.co/P8Qd25pEZI
2) Understanding Type I and Type II Errors: https://t.co/6JjlCWfwf0 by @schmarzo #abdsc #BigData #DataScience #Statistics #StatisticalLiteracy #DataLiteracy pic.twitter.com/M4JXCr4Ghi
Want your RMarkdown to be accessible to even *more* people? ✅
— Mara Averick (@dataandme) September 11, 2018
"Accessible R Markdown Documents" by @ajrgodfreyhttps://t.co/1ArCm9AsOa #rstats
Design Matrix https://t.co/eZ2bbpDzwV pic.twitter.com/h2n6cbPiu6
— Chris Albon (@chrisalbon) September 11, 2018
An online TensorFlow handbook (https://t.co/nYFykOO3yx ) by our ML GDE. It is based on Eager Execution to help developers get started with TensorFlow as painlessly as possible. Both Chinese & English version are available online. Open-sourced on GitHub → https://t.co/SkZo6vEnfc https://t.co/fflziGZnXr
— TensorFlow (@TensorFlow) September 11, 2018
I just noticed - One of my favorite "master tutorial lists" was recently updated - over 200 different tutorials across nearly every major branch of Machine Learning!!!
— Trask (@iamtrask) September 10, 2018
For #100DaysOfMLCode folks - this is... well... 200 days of projects!!! 🙌🥳https://t.co/1cOqWLAdM6
I hope you will to! Just out. It's *FREE* and creative commons lic.
— dj patil (@dpatil) September 9, 2018
If you like it give us a review. If we missed something, tell us. This is 0.1 release on a topic we all need to focus on https://t.co/08sfitiKJl