Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt


This concludes the first part of our journey into recommender systems. They are an extremely important part of today's online business models and their usefulness is ever-growing, in direct relation to the exponential growth of data generated by our connected software and hardware. Recommender systems are a very efficient solution to the information overload problem—or rather, an information filter problem. Recommenders provide a level of filtering that's appropriate for each user, turning information, yet again, into a vector of customer empowerment.

Although it's critical to understand how the various types of recommender systems work, in order to be able to choose the right algorithm for the types of problems you'll solve in your work as a data scientist, implementing production-grade systems by hand is not something most people do. As with almost everything in the realm of software development, it's best to use stable, powerful, and mature existing libraries when they're available...