June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Collecting data for use case one
We can collect data using a smartphone camera or a Raspberry Pi camera and prepare the dataset by ourselves, or download existing images from the internet (that is, via Google, Bing, and so on) and prepare the dataset. Alternatively, we can use an existing open source dataset. For use case one, we have used a combination of both. We have downloaded an existing dataset on pothole images (one of the most common road faults) from and updated the dataset with more images from Google images. The open source dataset (PotDataset) for pothole recognition was published by Cranfield University, UK. The dataset includes images of pothole objects and non-pothole objects, including manholes, pavements, road markings, and ...
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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.