May 2018
Beginner to intermediate
364 pages
7h 43m
English
Content preview from Hands-On Data Science with Anaconda
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Review questions and exercises
- What does optimization mean?
- What is an LPP? What are its uses?
- What is the difference between a global solution and a local solution?
- In what situations would our LPP program not converge? Give a few simple examples and possible solutions.
- Explain why we have the following weird result:
> f<-function(x)-2*x^2+3*x+1
> optim(13,f)
$par
[1] 2.352027e+75
$value
[1] -1.106406e+151
$counts
function gradient
502 NA
$convergence
[1] 1
$message
NULL
- What does quadratic equation mean?
- From where could we search all the R packages targeting optimization issues?
- What is the usage of the task view related to optimization?
- According to the related task view, how many R packages are associated with optimization, and how ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.