Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

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...