2019-08-01 update Things were a little different when I wrote this in 2017. These days I constantly see new/junior data scientists get rejected because they don’t have the experience. Even those who have an impressive portfolio of projects to show off that they have the technical know-how get thumbs down. I firmly believe this is a failure of employers, not the new generation of recently graduated data scientists entering the field.
Intro The other night I got TensorFlow™ (TF) and Keras-based text classifier in R to successfully run on my gaming PC that has Windows 10 and an NVIDIA GeForce GTX 980 graphics card, so I figured I’d write up a full walkthrough, since I had to make minor detours and the official instructions assume – in my opinion – a certain level of knowledge that might make the process inaccessible to some folks.
Introduction This post was inspired by a question about JAGS vs BUGS vs Stan: right, that's what got me confused! so they.. do the same thing? @RallidaeRule — Andrew MacDonald 🌈 (@polesasunder) January 10, 2017 Explaining the differences would be too much for Twitter, so I’m just gonna give a quick explanation here. 2020-05-18 update: Coming from a background of statistical inference in the context of academia and research using R, where these have been the prevalent PPLs for quite some time, I admittedly have a bit of a blind spot for PyMC3.
In the past year or two I’ve had several friends approach me about learning statistics because their employer/organization was moving toward a more data-driven approach to decision making. (This brought me a lot of joy.) I firmly believe you don’t actually need a fancy degree and tens of thousands of dollars in tuition debt to be able to engage with data, glean insights, and make inferences from it. And now, thanks to many wonderful statisticians on the Internet, there is now a plethora of freely accessible resources that enable curious minds to learn the art and science of statistics.
Inspired by The Visual Display of Quantitative Information by Edward R. Tufte The goal is to make the axes tell a better story about the data. This is done by turning the axes into quartile plots (cleaner boxplots). Usage Example: Only x and y are required, everything else is optional. qsplot( x = mtcars$wt, y = mtcars$mpg, main = "Vehicle Weight-Gas Mileage Relationship", xlab = "Vehicle Weight", ylab = "Miles per Gallon", font.