An in-depth exploration of Julias 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 perfectly suited for data science with a mature package ecosystem, and is now feature-complete. This book will help you get familiarized with Julias rich ecosystem, which is continuously evolving, allowing you to stay on top of your game.
You can dive in and learn the essentials of data science, with practical coverage of statistics and machine learning. You will learn to apply real-world skills and will develop knowledge on building statistical models and machine learning systems in Julia, with attractive visualizations. This book addresses challenges of real-world data science problems, including: data cleaning, data preparation, inferential statistics, statistical modelling, building high performance machine learning systems, and creating effective visualizations with D3 and Julia.
What you will learn
Apply statistical models in Julia for data-driven decisions
Explore techniques to visualize data using both Julia and D3
Use Julia to create self-learning systems using-cutting edge machine learning algorithms
Build a recommendation system in Julia
Dive into Julias deep learning framework to build an intelligent system
Explore real-world examples of implementing a deep learning system using Julia