About this event
Pie & AI is a series of DeepLearning.AI meetups independently hosted by community groups. This event is hosted by Nathan Crock. Special thanks to their support!
It is common ML practice to select hyperparameters with a validation set and assess model generalizability with a test set. In this workshop, we will motivate these concepts from first principles. We will interactively explore how different dataset partition decisions influence a real-world problem and then explain the observed behaviors with results from statistical learning theory. At the conclusion of the talk, participants will have an understanding of bias and variance in their estimators and how these concepts inform dataset training decisions.