"An Introduction to Probabilistic Programming" 218 pp 📖
— ML Review (@ml_review) October 1, 2018
Book Draft by @jwvdm @hyang144 @frankdonaldwood https://t.co/WYJu7NwGCO pic.twitter.com/AcX67FUut4
"An Introduction to Probabilistic Programming" 218 pp 📖
— ML Review (@ml_review) October 1, 2018
Book Draft by @jwvdm @hyang144 @frankdonaldwood https://t.co/WYJu7NwGCO pic.twitter.com/AcX67FUut4
[FREE 2600-page book] #Pandas — #Python Data Analysis Toolkit for #DataScience https://t.co/RxbQXXR3iv #abdsc #BigData #Analytics #AI #MachineLearning #Coding #DataScientists pic.twitter.com/TLem7ukrZ8
— Kirk Borne (@KirkDBorne) October 1, 2018
I implore young ML-ers to read abt the breaches of integrity that sunk Brian Wansink: "misreporting of research data, problematic statistical techniques, failure to properly document and preserve research results, and inappropriate authorship" https://t.co/EZplGyLq9h
— Zachary Lipton (@zacharylipton) September 30, 2018
👍 tutorial (and paper) 4 configs w/ {qgraph}:
— Mara Averick (@dataandme) September 29, 2018
📝 "(Mis)interpreting Networks: An Abbreviated Tutorial on Visualizations" by Payton Joneshttps://t.co/VrqgCgQoS8 via @EikoFried #rstats #networkvix pic.twitter.com/tmi58HWapk
That XKCD "curve fitting" comic was reproduced by @geospacedman with just a few lines of R code per panel. But any guesses how he reproduced the data so well? https://t.co/iY40hcc5TC #rstats
— David Smith (@revodavid) September 28, 2018
Hundreds of thousands of you have enrolled in a https://t.co/Ryb1M38abX course and started to further your career in Deep Learning. Now you can connect and learn with the global @deeplearningai_ community more easily: visit our new website and forums at https://t.co/Ryb1M38abX! pic.twitter.com/MMVNz5S5L1
— Andrew Ng (@AndrewYNg) September 28, 2018
A great series of interviews on the Pedagogy of NLP by @david__jurgens, @lucy3_li, starting with NLP experts @jurafsky and @YejinChoinka. If you're interested in teaching or in how NLP changes in the age of DL, check these out!https://t.co/cMWrbbC1z5https://t.co/FdqnkP8Yed
— Sebastian Ruder (@seb_ruder) September 28, 2018
Train Wide-ResNet, Shake-Shake and ShakeDrop models on CIFAR-10 and CIFAR-100 dataset with AutoAugment. https://t.co/BFFGMOjyCF https://t.co/lJVxLh1jJL
— hardmaru (@hardmaru) September 28, 2018
Machine Learning in a company is 10% Data Science & 90% other challenges. It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book
— Trask (@iamtrask) September 28, 2018
"Best Practices of ML Engineering"
This is a lifesaver #100DaysOfMLCode projecthttps://t.co/3FTq3zcZNf pic.twitter.com/hXC3WXtuLe
Interpretable Machine Learning by @ChristophMolnar released for free. https://t.co/x1w3Z3NI2b
— Jeong-Yoon Lee (@jeongyoonlee) September 28, 2018
Good practices in Modern Tensorflow for NLP: A notebook of best practice code snippets covering Eager execution, https://t.co/Dsgick98uu, and tf.estimator by @roamanalytics https://t.co/23YjhlkW9f
— Sebastian Ruder (@seb_ruder) September 27, 2018
📓 Am rereading my class notes from grad school, as well as from mentoring students for @Coursera and @EdX courses on statistics - and thought I'd share the most common mistakes when doing data analysis.
— 👩💻 @DynamicWebPaige 🔜 #APICityConf 🌇 (@DynamicWebPaige) September 27, 2018
✨Have counted 8 of 'em, with examples - please feel free to add your own!