Julia for Data Science

Anshul Joshi

ISBN 10: 1785289691 / ISBN 13: 9781785289699

Verlag: Packt Publishing Limited, 2016

Beschreibung

This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

Bestandsnummer des Verkäufers C9781785289699

Bibliografische Details

• Titel: Julia for Data Science
• Verlag: Packt Publishing Limited
• Erscheinungsjahr: 2016
• Sprache: Englisch
• ISBN-10: 1785289691
• ISBN-13: 9781785289699
• Einband: Paperback / softback
• Zustand: New

Über diesen Titel

Inhaltsangabe

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

Über die Autorin bzw. den Autor

Anshul Joshi is a data scientist with experience in recommendation systems, predictive modeling, neural networks, and high performance computing. His research interests are deep learning, artificial intelligence, computational physics, and biology. Most of the time, he can be caught exploring GitHub or trying anything new which he can get his hands on. He blogs on https://anshuljoshi.com/.