November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Summary
Machine learning is at the edge of the next wave, where we try to make ML ubiquitous in our everyday life. It has several advantages such as offline access, data privacy, and so on.
In this chapter, we looked at a new library from Google known as TensorFlow Lite, which has been optimized for deploying ML models on mobile and embedded devices. We understood the architecture of TensorFlow Lite, which converts the trained TensorFlow model into .tflite format. This is designed for inference at fast speed and low memory on devices. TensorFlow Lite also supports multiple platforms, such as Android, iOS, Linux, and Raspberry Pi.
Next, we used the MNIST handwritten digit dataset to train a deep learning model. Subsequently, we followed the ...
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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.