dirkschumacher

Draft: Applied Mixed Integer Linear Programming for Beginners

Format preferences

• virtual, morning hours Nashville, Tennessee time, as I am based in Berlin, 2 - 3 hours

Language in which the tutorial can be taught

• English

The title of the tutorial

• Applied Mixed Integer Linear Programming for Beginners

The broad topic it covers

• Discrete optimization, operations research

cderv

# installed by winget
winget install PowerShell-Preview
winget install git
git config --global user.name "Christophe Dervieux"
git config --global user.email christophe.dervieux@gmail.com
winget install rstudio
winget install vscode
winget install -e R
winget install Github.GithubDesktop

sunsided

{
    "bip": "172.39.1.5/24",
    "fixed-cidr": "172.39.1.0/25",
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

brshallo

library(tidyverse)
library(broom)
library(car)

param <- getOption("contrasts")
go_deviance <- param
# traditional `contr.sum` does not name levels, so use function from `car` package
go_deviance["unordered"] <- "contr.Sum"

raven4752

#put the these lines before importing any module from keras.
import tensorflow as tf

from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0" #only the gpu 0 is allowed
set_session(tf.Session(config=config))

amberjrivera

If You've Never Used Sklearn's Pipeline Constructor...You're Doing It Wrong

How To Use sklearn Pipelines, FeatureUnions, and GridSearchCV With Your Own Transformers

By Emily Gill and Amber Rivera

What's a Pipeline and Why Use One?

The Pipeline constructor from sklearn allows you to chain transformers and estimators together into a sequence that functions as one cohesive unit. For example, if your model involves feature selection, standardization, and then regression, those three steps, each as it's own class, could be encapsulated together via Pipeline.

Benefits: readability, reusability and easier experimentation.

robjhyndman

library(forecast)

fc <- function(y, h, xreg, newxreg) {
  fit <- auto.arima(y, xreg=xreg)
  forecast(fit, xreg=newxreg, h=h)
}

y <- ts(rnorm(100))
x <- matrix(ts(rnorm(100)),ncol=1)
tsCV(y, fc, xreg=x, h=1)

peterhurford

Advanced R, Abridged

"Advanced R" by Hadley Wickham is widely considered the best resource to improve your knowledge at R. However, going through it and answering every exercise takes a long time. This guide is designed to give you the most essential parts of Advanced R so that you can get going right away. It still will take a long time, but not as long.

--

1.) Quickly skim these chapters (without doing the exercises) to make sure you're familiar with the concepts:

• "Data Structures"
• "Subsetting"

danielappelt

D3 Links
--------
https://d3js.org/
https://bost.ocks.org/mike/bar/
https://jsfiddle.net/tLgp7qvv/ -> skalierender Bar Chart (abgewandelt von Teil 2 des Tutorials)

Javascript testen
-----------------
https://jsfiddle.net/

DavidGeistert

#install.packages('ReporteRs')
library('ReporteRs') # Load ReporteRs Package
pres.builder  <- pptx(template = '.../Master.pptx')
pres.filename <- ".../R-Meetup_Output.pptx"

# Show slide names
pres.builder

# Build Title slide
pres.builder  <- addSlide( pres.builder, "Title slide" ,bookmark = 1) # Slide name='Title slide', bookmark=1 <- overwrites first slide