June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Model evaluations
We have evaluated three different aspects of the models:
- Learning/(re)training time
- Storage requirement
- Performance (accuracy)
In terms of training time, in a desktop (Intel Xenon CPU E5-1650 v3 @3.5 GHz and 32 GB RAM) with GPU support, LSTM and CNN 1D on the ECG data took more than one hour, and MobileNet v1 on the acne dataset took less than 1 hour.
The storage requirement of a model is an essential consideration in resource-constrained IoT devices. The following screenshot presents the storage requirements for the three models we tested for the two use cases:
As shown, a saved model of LSTM took 234 MB of storage, CNN ...
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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.