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

Chapter 7. Machine Learning for Recommender Systems

I hope that you are now excited about the amazing possibilities offered by the recommender systems that we've built. The techniques we've learned will provide you with a tremendous amount of data-taming prowess and practical abilities that you can already apply in your projects.

However, there is more to recommendation systems than that. Due to their large-scale applications in recent years, as an efficient solution to the information overload caused by the abundance of offerings on online platforms, recommenders have received a lot of attention, with new algorithms being developed at a rapid pace. In fact, all the algorithms that we studied in the previous chapter are part of a single category, called memory-basedrecommenders. Besides these, there's another very important class or recommender, which is known as model-based.

In this chapter, we'll learn about them. We will discuss the following topics:

  • Memory-based versus model-based recommendation...