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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Toc

Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Julia also provides another function, summarystats()."

A block of code is set as follows:

ci(x::HypothesisTests.FisherExactTest) 
ci(x::HypothesisTests.FisherExactTest, alpha::Float64)
ci(x::HypothesisTests.TTest) 
ci(x::HypothesisTests.TTest, alpha::Float64)

Any command-line input or output is written as follows:

julia> Pkg.update() 
julia> Pkg.add("StatsBase")

New terms and important words are shown in bold.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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