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And now we're back.
So, we computed the or we defined the likelihood function for logistic
regression, we computed its derivative, we talked about simple real ascent algorithm
for finding the best parameters and for those who went through that full,
kind of slow, mathematical revision you saw where that gradient came from, but
that was totally optional.
We can go back and summarize where we are right now.
So now learning problem.
We have some training data, we threw this some feature extractor that gets us
H of X, and we saw a discretion model that says the probability that it's
a positive review is one over one plus E to the minus W transpose H.
And then we define the quality metric which is the likelihood function and
we use gradient ascent to optimize it to get w hat.
And the gradient descent algorithm is pretty simple and pretty intuitive.
Now, you're able to define what the quality metric is for logistic regression.
You can interpret the likelihood function as being kind of the probability that you
get training data right.
And you're going to maximize that.
We talked about the gradient ascent algorithm that does it with
really simple updates, and we had this optional section where we
derived a gradient ascent algorithm from scratch.
Next module, we're going to take this one step further and
explore the idea of regularization and over-fitting logistic regression,
which is a really important thing in practice.
So that's where we're going to go in the next module.
But now you're ready, even at this point, supplement your own logistic
regression algorithm from scratch, which is super exciting.
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