- virtual, morning hours Nashville, Tennessee time, as I am based in Berlin, 2 - 3 hours
- English
- Applied Mixed Integer Linear Programming for Beginners
- Discrete optimization, operations research
# 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
{ | |
"bip": "172.39.1.5/24", | |
"fixed-cidr": "172.39.1.0/25", | |
"runtimes": { | |
"nvidia": { | |
"path": "nvidia-container-runtime", | |
"runtimeArgs": [] | |
} | |
} | |
} |
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"
#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)) |
By Emily Gill and Amber Rivera
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
.
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) |
"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:
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/ |
#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 |