Gur Huberman points to this news article by Aaron Carroll, “Don’t Nudge Me: The Limits of Behavioral Economics in Medicine,” which reports on a recent study by Kevin Volpp et al. that set out “to determine whether a system of medication reminders using financial incentives and social support delays subsequent vascular events in patients following AMI compared with usual care”—and found no effect:
AI, Machine Learning and Data Science Roundup： December 2018
A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I’ve noted over the past month or so.
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We try to keep this blog mostly technical and business (as we assume that is what our readers are here for).
Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning
We recently published a series of articles looking at the top Python libraries, across Data science, Deep Learning and Machine Learning. As the year draws to a close, we thought we’d give you a special Christmas gift, and collate these into a KDnuggets official top Python libraries in 2018.
Data, movies and ggplot2
Yet another boring barplot?No!I’ve asked my students from MiNI WUT to visualize some data about their favorite movies or series.Results are pretty awesome.Believe me or not, but charts in these posters are created with ggplot2 (most of them)!
Heavy Tailed Self Regularization in Deep Neural Nets： 1 year of research
My talk at ICSI-the International Computer Science Institute at UC Berkeley. ICSI is a leading independent, nonprofit center for research in computer science.
Alternative approaches to scaling Shiny with RStudio Shiny Server, ShinyProxy or custom architecture.
Analyzing contact center calls—Part 1： Use Amazon Transcribe and Amazon Comprehend to analyze customer sentiment
Contact centers aiming to improve overall operational efficiency have an imperative to understand caller-agent dynamics. In part one of this two-part blog post series we’ll show you how you can use Amazon Transcribe and Amazon Comprehend to transform call recordings from audio to text and then run sentiment analysis on the transcripts. We will demonstrate how to use Amazon Transcribe to create text transcripts from an audio file. Afterwards, we’ll use Amazon Comprehend to analyze the call transcript, producing insights on keywords, topics, entities, and sentiment.
AzureStor： an R package for working with Azure storage
Storage endpoints
R Packages worth a look
Compute Expected Years of Life Lost (YLL) and Average YLL (yll)Compute the standard expected years of life lost (YLL), as developed by the Global Burden of Disease Study (Murray, C.J., Lopez, A.D. and World Health …