Book Image

F# for Machine Learning Essentials

By : Sudipta Mukherjee
Book Image

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
Table of Contents (16 chapters)
F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Vocabulary of collaborative filtering


The design of collaborative filters is influenced by two factors, which are as follows:

  • Users (the people for whom the recommendation is being provided)

  • Items (the products for which the recommendation is being provided)

Based on these two entities, there are two major varieties of collaborative filtering methods. The first of these methods takes the similarity between users to recommend items. This is known as User-User collaborative filtering or User k-Nearest Neighbors.

If the number of users of a recommender system is denoted by m and the number of items is denoted by n and if m >> n (m is much greater than n) then user-user collaborative filtering suffers from performance hiccups. In this case, item-item collaborative filtering (which relies on the similarity of the items) is often implemented.

In the next few sections, these two algorithms will be discussed at length.