- Eurostat, World Bank and others: https://ikashnitsky.github.io/2017/data-acquisition-two/
- Star Wars data: in dplyr, http://dplyr.tidyverse.org/reference/starwars.html
- Baby names data: babynames package, https://cran.r-project.org/web/packages/babynames/index.html
- Movies data: https://cran.r-project.org/web/packages/ggplot2movies/index.html ggplot2movies package
- Game of Thrones screen time: https://github.com/Preetish/GoT_screen_time
- Open Bike Data: https://github.com/ropensci/bikedata
- Tons of data through 538: https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html
- Public health data England: fingertipsR, https://cran.r-project.org/web/packages/fingertipsR/
- Financial data via Quandl: https://www.quandl.com/tools/r
- Cyclones: https://github.com/ropensci/rrricanesdat
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[core] | |
excludesfile = ~/.gitignore_global | |
pager = diff-so-fancy | less --tabs=4 -RFX | |
[difftool "sourcetree"] | |
cmd = opendiff \"$LOCAL\" \"$REMOTE\" | |
path = | |
[alias] | |
aa = add --all |
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## GUIDE TO AUTH2 Authentication in R Shiny (or other online apps) | |
## | |
## Mark Edmondson 2015-02-16 - @HoloMarkeD | http://markedmondson.me | |
## | |
## v 0.1 | |
## | |
## | |
## Go to the Google API console and activate the APIs you need. https://code.google.com/apis/console/?pli=1 | |
## Get your client ID, and client secret for use below, and put in the URL of your app in the redirect URIs | |
## e.g. I put in https://mark.shinyapps.io/ga-effect/ for the GA Effect app, |
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# My preferred defaults (may be changed in individual chunks) | |
opts_chunk$set(tidy=FALSE, warning=FALSE, message=FALSE, cache=TRUE, | |
comment=NA, verbose=TRUE, fig.width=6, fig.height=4) | |
# Name the cache path and fig.path based on filename... | |
opts_chunk$set(fig.path = paste("figure/", | |
gsub(".Rmd", "", knitr:::knit_concord$get('infile')), | |
"-", sep=""), | |
cache.path = paste(gsub(".Rmd", "", knitr:::knit_concord$get('infile') ), | |
"/", sep="")) |
Maybe you've heard about this technique but you haven't completely understood it, especially the PPO part. This explanation might help.
We will focus on text-to-text language models 📝, such as GPT-3, BLOOM, and T5. Models like BERT, which are encoder-only, are not addressed.
Reinforcement Learning from Human Feedback (RLHF) has been successfully applied in ChatGPT, hence its major increase in popularity. 📈
RLHF is especially useful in two scenarios 🌟:
- You can’t create a good loss function
- Example: how do you calculate a metric to measure if the model’s output was funny?
- You want to train with production data, but you can’t easily label your production data
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.