February 2018
Intermediate to advanced
262 pages
6h 59m
English
Content preview from Deep Learning with PyTorch
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Model ensembling
There could be times when we would need to try to combine multiple models to build a very powerful model. There are many techniques that can be used for building an ensemble model. In this section, we will learn how to combine outputs using the features generated by three different models (ResNet, Inception, and DenseNet) to build a powerful model. We will be using the same dataset that we used for other examples in this chapter.
The architecture for the ensemble model would look like this:
This image shows what we are going to do in the ensemble model, which can be summarized in the following steps:
- Create three models
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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.