Please watch the video. (Video starts) GREEN FLOAT is a new conceptual model for environmentally friendly cities.

Second step, conceptual synesthesia.

And so this is a great conceptual model to have of the world, unless you're a particle physicist.

So the equation is conceptual.

Retargeting is the process of transposing that data onto another model.

A 3D model is made by piling up the slice data.

line is just not a very good model for this data set.

Here's the conceptual diagram of the whole idea.

And so you can model p of x based on this sort of data.

And all of Julie's data has been pre entered into my interactive software model.

It's kind of backwards compatibility, because you're often adding features, tweaking your data model.

It's conceptual. It's universal. It's beyond words and numbers.

That is the conceptual way of how it works.

I took a more conceptual approach to the valentine.

And here's the resulting data that comes from that, the model that comes from that.

Now that's really the conceptual way to imagine absolute value.

Clearly a linear regression model is a very poor one to explain this data over here.

At that point, there was no physical model, it was just data on a computer somewhere.

The one on the left, shows the concordance cosmology model, plotted against the data, as you can see it goes beautifully through the data points, which is in a sense another vindication for the concordance model.

Data, data, data...

Often it's language which doesn't land for you. It's conceptual language.

He made this amazing conceptual statement that blew up everyone's minds.

Bing! That's data. Data, data, data.

Model

Model

Model

Model

So I'll compare model one to model three and I'll compare model two to model three.

The model of expectation maximization is that each data point is generated from what's called a mixture.

And this data set happens to be fittable by a linear regression model without any residual error.

So, that was model selection and how you can take your data and split it into a train validation and test set, and use your cross validation data to select model and evaluate it on the test set.

And the probability of those data points for any Gaussian model mu and sigma squared is the product of any individual of data likelihood x,i.

Printer model

Printer Model

Atom Model

Camera Model

UML Model

Printer model

Printer Model

Color Model

Unspecified Model

You model.

Model, huh?

First model walks, second model duplicates, then elaborates.

All kinds of conceptual things, they have names now that start with HTTP.