Julia for Data Science: high-performance computing simplified

Anshul Joshi

$29.99 ~~$43.99~~

4.5 (6 Ratings)

eBook Sep 2016 346 pages 1st Edition

Anshul Joshi

$29.99 ~~$43.99~~

4.5 (6 Ratings)

eBook Sep 2016 346 pages 1st Edition

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Key benefits

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

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 visualizations using Julia.

Who is this book for?

This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics.

What you will learn

Apply statistical models in Julia for data-driven decisions
Understanding the process of data munging and data preparation using Julia
Explore techniques to visualize data using Julia and D3 based packages
Using Julia to create self-learning systems using cutting edge machine learning algorithms
Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models
Build a recommendation engine in Julia
Dive into Julia's deep learning framework and build a system using Mocha.jl

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Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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RareComplexCollectionOfMatter Aug 17, 2020

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Rahul Dec 17, 2017

As someone who knew nothing about data science and machine learning models, this book proves to be a great asset for people looking out for serious and immersive content over the topic. The author explains each and every topic in detail and uses Julia code, which btw is something that every modern data scientist should be looking out for. Overall, this is a great book and I would highly recommend it.

Deepankar A. Jan 25, 2017

The way the book is written is really amazing with various practical examples. This actually gives a good insights of how to use Julia with Data Science. The language is easy by comparing the complexity the book is dealing with so it is easy for starters to start with. It is a must read if one is adopting Julia as a language for Data Science.

Amazon Customer Jan 15, 2017

As is the case with many Packt publications this work is a genuinely amateurish effort! By way of example, consider pages 23 through 26:1) Two examples of Julia code are misplaced: a) Page 23 - "pmap refers to parallel map. For example:" the Julia code which follows uses @parallel, not pmap. The example of the Julia code using pmap is displayed on Page 24 in the section titled "Julia's key feature - multiple dispatch". b) Page 25 includes a discussion of multiple dispatch. The first paragraph discusses the development of two functions, one accepting two Float64 arguments, the other two Number arguments. Unfortunately the Julia example code intended to illustrate multiple dispatch is code related to a discussion of function errors due to type mismatches which occurs on the Page 24. The proper example used to illustrate this is displayed on Page 26 following a paragraph discussing ambiguous function calls. In short, nobody has bothered to insure that the example Julia code is correctly placed within the text!2) Nobody seems to have bothered editing this manuscript for reasonable English or computer language literacy. Consider the following sentences from Page 24, " Traditionally object-oriented languages consider only the first argument in dispatch. Julia is different as all of the function's arguments are considered (not just only [sic] the first) and then it choses which method should be invoked." Why we need "just" and "only" to modify "the first" I have yet to discern. Perhaps more fundamental is the author's ignorance of multiple dispatch in object oriented languages: CLOS implements it directly and C++ and Java, for example, have emulation capabilities and/or library extensions for implementation.Finally, and this is based on a very brief reading, but the discussions of a) "Deep Dive into Inferential Statistics" (remarkable only for its shallowness), b) "Supervised Machine Learning" (the author is in need of adult supervision), c) "Unsupervised Machine Learning" (well, it certainly is unsupervised), are wholly inadequate - little more than abbreviated code recipes and eyes-wide-shut indifference to the very real complexities of these topics!I am certain that the author is enthusiastic about Julia but I am altogether uncertain that he, his reviewers, the editors and Packt have exerted sufficient effort to claim technical credibility and command for this volume's price.If we compare this work with similarly themed work for R or Python as published by O'Reilly or Springer, for example, its defects emerge even more pronounced. With this volume (and Voulgaris's volume of the same name) readers find themselves in unseemly alleys better gotten out of before nightfall.All deeply regrettable as the Julia community generally is notable for its technical rigor.

Amazon Customer Dec 27, 2016

I would definitely recommend this book. I have worked on many projects in the past and have used Python,R and Scala, this book has added an entire new area for me to work on. It is well structured and was easy to go through, a good job by the author.

Julia for Data Science: high-performance computing simplified

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