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

Always Best

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.

Optimize for large files

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

We have to be correct.

I know that you might be wondering how is it that the optimization problem I wrote down in the previous slide, how does that lead to this large margin classifier.

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.

I wish to find an optimal policy for all these states that with maximum probability leads me to the absorbing state plus 100, and as I just discussed, I assume there's 4 different actions, north, south, west, and east that succeed with probability 80 provided that the corresponding grid cell is actually attainable.

Best

Optimized

Compaction

Optimize

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.

The goal is to control cancer.

Compacting database

Best chance of keeping those thing out.

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.

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

Optimum Power Calibration failed.

exception 0 divided by 0 raises an exception.

A serial terminal optimized for logging and file capture

Here's another way to look at the challenge of optimization.

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

In this video we'll define something called the cost function.

line of code on the right will do is it use

Compact Folder

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

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

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.

Very often it's static.

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

Now let's go and implement this.

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

Unknown Optimization

And so, it's a very simple overall Friedmann equation.

Compact the current database project

You are preventing some refactoring, some restructurings of that code because it would it would break the optimization you know the optimization was made with certain assumptions, that's almost the definition of an optimization is that if we assume certain things, then we don't have to do some of these operations, and that's the way to make things faster is to not do some of the things that you were previously doing.

When you think of processing these 20 million different genes or trying to optimize processes to produce octane or to produce pharmaceuticals, new vaccines, we can just with a small team, do more molecular biology than the last 20 years of all science.

And then more recently, they really began to do reverse engineering on this beast and figure out just what the parts were, how they worked and how they might be optimized how they might be made better.

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.

Another thing to notice is there may be many peaks on the moral landscape

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

You don't need to scale ahead of time.