n the last chapter, you saw how it’s possible to accidentally train an ML system in a way that causes it to give the wrong answer, by introducing bias into your training examples.
In this chapter, you’ll see how bias is sometimes introduced intentionally to influence the answers that an ML system gives. You’ll create an app that recommends movies to people based on the sort of films that they like. But you’ll train your model in a way that lets you affect the recommendations.
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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.
n the last chapter, you saw how it’s possible to accidentally train an ML system in a way that causes it to give the wrong answer, by introducing bias into your training examples.