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Learning AI by doing: My new series of posts

The first in a series of six posts about my experiences learning how to train a machine learning system has just been posted here. There’s no code and no math in it. Instead it focuses on the tasks and choices involved in building one of these applications. How do you figure out what sort of data to provide? How do you get that data into the system? How can you tell when the system has been trained? What types of controls do the developers have over the outcomes? What sort of ways can I go wrong? (Given that the title of the series is “The Adventures of a TensorFlow.js n00b” the answer to that last question is: Every way.)

I was guided through this project by Yannick Assogba, a developer in the machine learning research group — People + AI Research –I’m embedded in at Google as a writer in residence. Yannick is natural born teacher, and is preternaturally patient.

The series is quite frank. I make every stupid mistake possible. And for your Schadenfreude, five more posts in this series are on their way…

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