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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 2. Anaconda Installation FREE CHAPTER 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

Packages for optimization in R

There are many R packages available for various types of optimization, such as optimization, MlBayesOpt, rgenoud, colf, and mize. The following table offers a partial list:

#
Name
Description

1

dtwclust

Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

2

CVXR

Disciplined Convex Optimization

3

IROmiss

Imputation Regularized Optimization Algorithm

4

subplex

Unconstrained Optimization using the Subplex Algorithm

5

GPareto

Gaussian Processes for Pareto Front Estimation and Optimization

6

OOR

Optimistic Optimization in R

7

ROI

R Optimization Infrastructure

8

lbreg

Log-Binomial Regression with Constrained Optimization

9

PEIP

Geophysical Inverse Theory and Optimization

10

dfoptim

Derivative-Free Optimization

11

SPOT

Sequential...

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