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Hands-On Data Science with Anaconda

You're reading from  Hands-On Data Science with Anaconda

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Pages 364 pages
Edition 1st Edition
Languages
Authors (2):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan
James Yan James Yan
Profile icon James Yan
View More author details

Table of Contents (15) Chapters

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Review questions and exercises

  1. What does optimization mean?
  2. What is an LPP? What are its uses?
  3. What is the difference between a global solution and a local solution?
  4. In what situations would our LPP program not converge? Give a few simple examples and possible solutions.
  5. 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
  1. What does quadratic equation mean?
  2. From where could we search all the R packages targeting optimization issues?
  3. What is the usage of the task view related to optimization?
  4. According to the related task view, how many R packages are associated with optimization, and how do we install them all at once?
  5. From the Prof. French Data Library, download the return data for 10 industries...
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