The scaling factor was 1.

But it's just a scaling factor.

The 2 is just kind of a scaling factor.

We have a wider opening u because our scaling factor is

So some constant factor, some type of scaling factor, times x minus the x value of our vertex.

Select a length or a segment whose length is the factor of the scaling...

Select the first of two lengths whose ratio is the factor of the scaling...

Select the second of two lengths whose ratio is the factor of the scaling...

Scaling

Scaling

Scaling...

Scaling

So all we're doing is we're adding the vectors, and we're just scaling them up by some scaling factor, so that's why it's called a linear combination.

No scaling

Scaling Algorithm

Smooth scaling

Scaling capability

Smooth scaling

Logarithmic Scaling

Scaling Options

So you literally just go down 1 4 not just 1 4, 1 4 times this scaling factor.

Time Axis Scaling

Enable automatic scaling

CPU frequency scaling policy

There is a scaling.

That was feature scaling.

The ability of one to affect many is scaling exponentially, and it's scaling for good and it's scaling for evil.

This is the Laplace transform of f of t times some scaling factor, and that's what we set out to show.

And here's scaling of cities.

So it's really just scaling.

So they're mapping the social scaling onto the geometric scaling it's a conscious pattern.

Because we're just scaling them up.

Factor

Factor

It's the WTF factor. WTF factor.

You can pattern match that this scaling factor right here is going to be the same thing as this 1 over 2 b minus k.

Select the center point of the scaling...

Whether to use automatic or custom scaling.

You're just scaling it by that amount.

Dim factor

Zoom factor

Cooling factor

Scale factor

Shear factor

Gradient factor