A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.
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A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.
Once we have the data we need—thousands of sample games—how do we turn it into something the ML can train itself on? That means understanding how training works, and what a model is.
Tic-Tac-Toe the Hard Way
A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.
