Always Best

Best

Optimized

Compaction

Optimize

Optimization

We have to be correct.

Compact Folder

Compacting database

Optimize for large files

Unknown Optimization

exception 0 divided by 0 raises an exception.

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 there's plenty of room for creativity in optimization.

Best chance of keeping those thing out.

Bugfixes and optimizations

Optimum Power Calibration failed.

The goal is to control cancer.

Performing Optimum Power Calibration

AutoOpt Molecule

Compact the current database project

I save it every time we're in this loop.

A serial terminal optimized for logging and file capture

So,that's gives us our overall optimization objective function for the Support Vector

Remember how are we using optimization okay?

A cross platform tool for optimizing PNG and JPG files.

Compact Now

And in the next video

All right.

Here is what I mean, to give you a concrete example, suppose I had a minimization problem that you know minimize over a real number u of u minus 5 squared, plus 1, right.

So let's try to understand the optimization problem in the context of, what would it take to make this first term in the objective equal to zero, because you know, maybe we'll set C to some huge constant, and this will hope, this should give us additional intuition about what sort of hypotheses a support vector machine learns.

Here I've changed the middle statement to be b equals 2.

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

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.

If C is very, very large, then when minimizing this optimization objective, we're going to be highly motivated to choose a value, so that this first term is equal to zero.

Its not particularly dynamic and it doesn't really change the way you think about programming.

We have to get the same answer, just in less time.

Over the entire course, we've seen lexing, parsing we just saw optimizing.

This is the critical rule of optimization.

Its computational time is increasing

In the next video, we'll start to talk about an algorithm for trying to optimize the cost function.

Somehow we come to the conclusion that F(a) is always equal to G(a).

The current project has to be closed before compacting the database. It will be open again after compacting. Do you want to continue?

line of code on the right will do is it use

You don't need to scale ahead of time.