March 2018
Intermediate to advanced
484 pages
10h 31m
English
book
Deep Learning with TensorFlow - Second Edition
by Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim, Motaz Saad
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Chapter 9. Recommendation Systems Using Factorization Machines
Factorization models are very popular in recommendation systems because they can be used to discover latent features underlying the interactions between two different kinds of entities. In this chapter, we will provide several examples of how to develop recommendation system for predictive analytics.
We will see the theoretical background of recommendation systems, such as matrix factorization. Later in the chapter, we will see how to use a collaborative approach to develop a movie recommendation system. Finally, will see how to use Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems.
In summary, the following topics will be covered ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.