May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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12. Dimensional Reduction and Latent Variable Models
12.1 Introduction
Now that you have the tools for exploring graphical models, we’ll cover a few useful ones. We’ll start with factor analysis, which finds application in the social science and in recommender systems. We’ll move on to a related model, principal components analysis, and explain how it’s useful for solving the collinearity problem in multiple regression. We’ll end with ICA, which is good for separating signals that have blended together. We’ll explain its application on some psychometric data.
One thing all of these models have in common is that they’re latent variable models. That means in addition to the measured variables, there are some unobserved variables that underlie ...
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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.