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

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

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.

Always Best

Optimize for large files

Best

Optimized

Compaction

Optimize

Optimization

We're never changing the meaning of the program.

Bugfixes and optimizations

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

The goal is to control cancer.

We have to be correct.

Now let's go and implement this.

Compact Folder

Compacting database

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.

This is the critical rule of optimization.

Unknown Optimization

exception 0 divided by 0 raises an exception.

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.

Best chance of keeping those thing out.

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

We're only checking for the B the right position to be the constant number.

Job two make it fast.

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.

Compact Now

Optimum Power Calibration failed.

What if I want to fix your program

Compacts the current database project, so it will take less space and work faster.

Performing Optimum Power Calibration

AutoOpt Molecule

Compact the current database project

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

And I'm going to do that by transforming the parse tree directly.

That way, it makes it much easier to check if new statements invalidate a cached response.

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.

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

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

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

So this is just a different way of controlling the trade off or just a different way of parametrizing how much we care about optimizing the first term versus how much we care about optimizing the second term.

Now what correct is, it can vary depending on what your actual goals are, but the program should work as you intend it and once you start optimizing things that shouldn't change.