February 2018
Intermediate to advanced
450 pages
11h 27m
English
Content preview from Deep Learning By Example
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Feature Engineering and Model Complexity – The Titanic Example Revisited
Model complexity and assessment is a must-do step toward building a successful data science system. There are lots of tools that you can use to assess and choose your model. In this chapter, we are going to address some of the tools that can help you to increase the value of your data by adding more descriptive features and extracting meaningful information from existing ones. We are also going to address other tools related optimal number features and learn why it's a problem to have a large number of features and fewer training samples/observations.
The following are the topics that will be explained in this chapter:
- Feature engineering
- The curse of dimensionality ...
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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.