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

Comparing the memory-based versus model-based recommenders

It is important to understand the strengths and weaknesses of both memory-based and model-based recommenders so that we can make the right choice according to the available data and the business requirements. As we saw in the previous chapter, we can classify recommender systems according to the data they are using and the algorithms that are employed.

First, we can talk about non-personalized versus personalized recommenders. Non-personalized recommenders do not take into account user preferences, but that doesn't make them less useful. They are successfully employed when the relevant data is missing, for example, for a user that is new to the system or just not logged in. Such recommendations can include the best apps of the week on the Apple App Store, trending movies on Netflix, songs of the day on Spotify, NY Times bestsellers, Billboard Top 10, and so on.

Moving on to personalized recommender systems, these can be further split...