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

Classifying recommender systems

Different business needs—from suggesting related products after buying your new laptop, to compiling the perfect driving playlist, to helping you reconnect with long lost schoolmates—led to the development of different recommendation algorithms. A key part of rolling out a recommender system is picking the right approach for the problem at hand to fully take advantage of the data available. We'll take a look at the most common and most successful algorithms.

Learning about non-personalized, stereotyped, and personalized recommendations

The simplest types of recommendations, from a technical and algorithmic perspective, are the non-personalized ones. That is, they are not customized to take into account specific user preferences. Such recommendations can include best-selling products, various top 10 songs, blockbuster movies, or the most downloaded apps of the week.

Non-personalized recommendations are less challenging technically, but also considerably less powerful...