That equals zero.

If Mu equals zero, sigma equals 1.

So you get x plus five equals zero or x plus five equals zero.

The foremost is that if we're blank slates, then, by definition, we are equal, because zero equals zero equals zero.

So over here it equals zero

And sure enough that equals zero.

So, you know, let's break it out. Let's take theta zero equals 360 theta one equals zero.

Notice y equals zero, x plus one.

Let's assume we have a robot, and let's call arbitrarily the initial location x equals zero and y equals zero.

We compute capital D as follows and we have two separate cases for j equals zero and j not equals zero.

If Mu is equal to zero it equals 1.

If theta zero equals 0 and theta one equals 0.5, then the hypothesis will look

If it's less than 0.5, you predict y equals zero.

The case of j equals zero corresponds to the bias term so when j equals zero that's why we're missing is an extra regularization term.

Newton's second law of motion net force equals mass times acceleration tells us that if the acceleration equals zero, the net force must equal zero.

And then you would get zero equals zero, which is true for any x that you pick.

In the case of a Maclaurin we're picking x equals zero, and it equals every derivative of this function.

I get x squared plus ten x plus twenty five equals zero.

So we could say one hundred to the zero power equals one.

Well, f of x is equal to, it equals f of zero times x to the zero over zero factorial, that's just one, right?

So now we can say that x plus two equals zero, and x plus four is equal to zero.

And so this is, with, you know, theta zero equals maybe about 800, and theta one equals maybe a 0.15 .

And I get two x squared plus twenty x plus fifty equals zero.

You get 4 over here, which is minus zero plus 4 equals 4.

Concretely, because Y must be either zero or one, we know that the probability of Y equals zero, plus the probability of Y equals one, must add up to one.

This is a robot that points in the x direction, because θ equals zero.

E to the minus of that term approaches zero one over one equals one.

And finally if theta zero equals 1 and theta one equals 0.5 then we end up with the hypothesis that looks like this.

And this is just saying that the probability of Y equals zero plus the probability of Y equals one must be equal to one.

So it's approximately linear around F of X equals zero, but it levels off to zero and one as we go to the extremes.

inaudible equals sum 1 through M of tetha zero plus tetha one, XI minus YI squared.

There's my regression, coefficients. The intercept is the predicted score on Y when X equals zero.

So, if we measured zero point zero zero seven zero zero kilometers.

So for something to be continuous at x equals zero lets think about what has to happen.

So what's zero, plus zero, plus zero, plus zero?

Zero plus zero plus zero ten times is still zero.

Equals

equals

Now if two expressions or two numbers times each other equals zero, that means that one of those two numbers or both of them must equal zero.

1, zero, minus 1, zero, 1, zero.

If I had this equation. x squared minus x minus x equals zero, that you could factor that as x minus 3 and x plus 2 equals 0.

If it equals zero, we go ahead and continue on because no tile needs to be drawn there.

We could have rewritten this as just y equals one, because zerox is the same things as zero.

So 1, zero, minus 1, zero, positive, zero, negative, zero, positive.

One, one, point zero zero one, zero zero one, dot dot dot.