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Julia for Data Science

You're reading from  Julia for Data Science

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

Table of Contents (17) Chapters close

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 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

Revisiting linear algebra


Linear algebra is a widely used branch of mathematics. Linear algebra is a part of discrete mathematics and not of continuous mathematics. A good understanding is needed to understand the machine learning and deep learning models. We will only revise the mathematical objects.

A gist of scalars

A scalar is just a single number (as opposed to a large portion of alternate objects examined in linear algebra, which are generally arrays of various numbers).

A brief outline of vectors

A vector is an organized collection or an array of numbers. We can recognize every individual number by its index in that list. For example:

x = [x1, x2, x3, x4 ..... xn]

  • Vectors can also be thought of as identifying points in space.

  • Each element represents the value of coordinate along a different axis.

  • We can also index the positions of these values in the vector. Therefore, it makes it easier to access the specific value of the array.

The importance of matrices

  • A matrix is a two-dimensional array...

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