Julia for Data Science

Anshul Joshi

ISBN 10: 1785289691 / ISBN 13: 9781785289699

Published by Packt Publishing, 2016

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering.

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Bibliographic Details

• Title: Julia for Data Science
• Publisher: Packt Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 1785289691
• ISBN 13: 9781785289699
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 19D millimeters 235 millimeters width by 191 millimeters height by 19 millimeters depth
• Weight: 647 grams

About this title

Synopsis

Key Features

• An in-depth exploration of Julia's growing ecosystem of packages
• Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization
• Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets

Book Description

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

About the Author

Anshul Joshi is a Consultant for Data Science in Xebia. He started studying about machine learning and distributed computing during college. He has keen interest in data science, deep learning, and neural networks and has some experience working with Spark and MLlib. Anshul is passionate about his job and is a technology enthusiast and tries to stay up to date with the latest developments.