Julia for Data Science

Julia for Data Science

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 perfectly suited for data science with a mature package ecosystem, and is now feature-complete. This book will help you get familiarized with Julia's 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 Julia's deep learning framework to build an intelligent system - Explore real-world examples of implementing a deep learning system using Julia 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.