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Let's take a final minute to summarize what we have seen in today's module.
Around scaling to large datasets and online learning.
Now, we use gradient to scale to large datasets, but
this is not the only approach that's used.
In fact, a lot of scaling that we do is about dealing with multicore processors.
So, the fact that processors have multiple cores and it can do parallel computation.
And then, using large computers clusters with 10 of machines, 100 machines,
1,000 machines, 100,000 machines, and so on.
And this requires new kinds of machinery algorithms which are called distributed
and parallel algorithms that can be spread out over a thousand machine.
It's a very important topic.
It's actually what a lot of my personal research is about.
How do you do this written machine or any algorithms, unfortunately is not something
that are not going to have time to cover in the current course.
As a summary we seen that a small modification of your gradient
algorithm can incredible improvement in the overall running time of the approach,
and that makes a huge difference in practice.
The many practical challenges associated in gradient is extremely useful.
And the same kind of techniques that we saw here can also be used for
online learning which is a whole different type of machine learning which
is also of great practical significance.
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