Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

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Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

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LLH. This is episode 27 Hyperparameters part one.

Today we're going to be talking about hyperparameters.

This is going to be a two-part episode.

Got a little bit longer than I thought it would.

But let's dive right in.

What are hyperparameters?

Well, we've talked about hyperparameters before, by comparison to parameters.

There are anything that the human decides on. Parameters are the numbers that the machine learning

model learns in its learning process. So in linear and logistic regression, your theta

parameters are these weights in front of the coefficients. There are these numbers that the model

learns. Parameters are the bit that the machine learning model learns.

Hyperparameters are any sort of knobs and dials that you as the human are in control of.

So there's some obvious cases of hyperparameter selection, for example, with regularization that we'll talk about in the next episode, the selection of

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