It's the best explanation.

This isn't necessarily how you would normally do it, but it's good enough to serve our purposes right now.

This is the very best method.

This is the critical rule of optimization.

So you know suppose this is your initial value of theta one over here and it's already at a local optimum or the local minimum.

We can just do the same thing for the second elements of A and B, multiply together.

You don't need to scale ahead of time.

So there's plenty of room for creativity in optimization.

So, it's going across a stripe at a time, breadth first.

So for bimodal data, or for multimodal data, which is data with even more than two bumps, the mode is something you should really calculate.

We know that we are optimized as human beings, to do hard and meaningful work.

This may seem a little abstract, but let me firm it up with a concrete example.

In value iteration, we recursively calculate the value function so that in the end, we get what's called the optimal value function.

That leads us to this Nash equilibrium point, and the values of that outcome are 3 for each party.

And now when we look up nitrogen, we get the value that we assigned to nitrogen, which is 8.

Best

Optimized

Best matches

Compaction

Optimize

Optimization

It's the best time to fish.

Yes, I understand.

With results like this. vector graphics are a great artistic medium.

This is also ideal as a gift.

But often domain specific languages do a better job at it.

Rhythm and Blues Revue, for your dancing pleasure.

We maximized the posterior probability of data logarithmic is a monotonic function, and I put a minus sign over here so the optimization problem becomes a minimization problem.

So one of the topics that we cover in this class is optimization, making a program faster but having it compute the same result, and I like optimization a lot.

Optimization is sometimes described as conservative because if you're not absolutely certain that an optimization is okay, just be conservative and don't do it, and you'll be fine.

Best for spot colours

That is, here's one thing we can say.

A little bit of this. A little bit of that.

You can see that in a maze like this you would expand node over here before we touch any of the nodes over here.

Realize that even states I'm not likely to every reach,

What this says is based on the outcome, we believe for the parameter µ.

I've also graphed, with a dotted line, the policy loss.

The goal is to control cancer.

Then I want you to ask one more question or answer one more question which is are these searches optimal?

But look. Just by adding shelves. This is just plain plywood...

You can argue if you do this perfectly well, you wouldn't even have to plan strategies.

What's really remarkable here is that the car is able to plan really complex maneuvers to the goal.

You're the best man for the job.

If so, we return True, and the optimization is okay.

Always Best