November 2017
Intermediate to advanced
304 pages
6h 58m
English
Content preview from Machine Learning with TensorFlow 1.x
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Summary
In this chapter, we have implemented a complete real-life production, from training to serving a deep learning model. We also created a web interface in a Flask app so that users can upload their images and receive results. Our model can automatically be fine-tuned every day to improve the quality of the system. There are a few things that you can consider to improve the overall system:
- The model and checkpoints should be saved in cloud storage.
- The Flask app and TensorFlow Serving should be managed by another, better process management system, such as Supervisor.
- There should be a web interface so that the team can approve the labels that users select. We shouldn't rely completely on users to decide the training set.
- TensorFlow ...
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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.
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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.