Explore >> Select a destination
|
You are here 
|
www.rdatagen.net | ||
| | | | |
www.cedricscherer.com
|
|
| | | | | A step-by-step tutorial explaining how my visualizations have evolved from a typical basic ggplot. Here, I transform a basic boxplot into a compelling and self-explanatory combination of a jittered dot strip plot and a lollipop plot. | |
| | | | |
fharrell.com
|
|
| | | | | In randomized clinical trials, power can be greatly increased and sample size reduced by using an ordinal outcome instead of a binary one. The proportional odds model is the most popular model for analyzing ordinal outcomes, and it borrows treatment effect information across outcome levels to obtain a single overall treatment effect as an odds ratio. When deaths can occur, it is logical to have death as one of the ordinal categories. Consumers of the results frequently seek evidence of a mortality reduct... | |
| | | | |
www.johnmyleswhite.com
|
|
| | | | | Over the last few months, I've had a lot of conversations with people about the use of winsorization to deal with heavy-tailed data that is positively skewed because of large outliers. After a conversation with my friend Chris Said this past week, it became clear to me that I needed to do some simulation studies to understand the design space of techniques for dealing with outliers. In this post, I'm going to write up my current understanding of the topic. | |
| | | | |
scorpil.com
|
|
| | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | ||
