October 2017
Intermediate to advanced
330 pages
7h 7m
English
Content preview from Python Deep Learning Cookbook
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Extracting bottleneck features with ResNet
The ResNet architecture was introduced in 2015 in the paper Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385). ResNet has a different network architecture than VGG. It consists of micro-architectures that are stacked on top of each other. ResNet won the ILSVRC competition in 2015 and surpassed human performance on the ImageNet dataset. In this recipe, we will demonstrate how to leverage ResNet50 weights to extract bottleneck features.
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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.