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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda FREE CHAPTER 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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