February 2018
Intermediate to advanced
378 pages
10h 14m
English
Content preview from Machine Learning with Swift
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SqueezeNet
The architecture was proposed by Iandola et al. in 2017 for use in autonomous cars. As the baseline, researchers took the AlexNet architecture. This network takes 240 MB of memory, which is pretty much the equivalent of mobile devices. SqueezeNet has 50x fewer parameters, and achieves the same level of accuracy on the ImageNet dataset. Using additional compression, its size can be reduced to about 0.5 MB.
SqueezeNet is built from the fire modules. The objective was to create a neural network with a small number of parameters, but preserving the competitive level of accuracy. It was done with the following approaches:
- Reduce the network size by replacing the 3 x 3 filters with 1 x 1 filters. Here, by replacing the 3 x 3 filter ...
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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.
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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.