April 2020
Intermediate to advanced
380 pages
9h 24m
English
Content preview from Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
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Summary
In this project, we covered the concepts of reinforcement learning and why they're popular among developers for creating game-playing AIs. We discussed AlphaGo and its sibling projects by Google DeepMind and studied their working algorithms in depth. Next, we created a similar program for playing Connect 4 and then for chess. We deployed the AI-powered chess engine to GCP on a GPU instance as an API and integrated it with a Flutter-based app. We also learned about how UCI is used to facilitate stateless gameplay for chess. After this project, you are expected to have a good understanding of how we can convert games into reinforcement learning environments, how to define gameplay rules programmatically, and how to create self-learning ...
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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.