In this video, we are going to summarize everything we learned in the section, this section focused
on convolutional neural networks.
Now for us, since we already knew how a Anan's work, the only matter at hand was to understand convolution
and why it's appropriate in deep learning.
I did give you the option to skip this part if you are a beginner at math and just wanted to see the
code so only those who watch the convolution lectures will have a good understanding of how it works
and why we use it.
These videos taught you convolution from multiple different perspectives.
I think this is useful because people often believe convolution is abstract, but in fact it's very
intuitive from the perspective of image processing.
We first looked at how to perform convolution.
That is what convolution actually does in terms of multiplication and addition.
Surprisingly, all it takes is a bit of 3rd grade math.
Now, understanding what that does takes a more mature perspective.
So it's not just about the mechanics.
We learn that convolution and deep learning is really what everyone else calls cross correlation.
And in fact, correlation is probably a much better name, since most people have a better understanding
of what this word means.
Correlation is really just two words put together correlation, the relationship between two things.
Thus it tells us how related two things are.
Time series.
In our case, this leads us to the idea that convolution is a pattern matcher.
The convolution filter is like a template that we want to match with and the input image is the thing
where we want to find relevant patterns.
We also noted that the Arima models we learned about are really just convolutions, a Remar Perimeter's
or Arima weights are really just a convolution filter.
Thus we are already done convolution in this course without knowing we did.
After gaining a deeper understanding about convolution, we then learned how to build Xians, we observe
that after learning how to build Anan's building, CNN's is pretty easy.
Just a few new layers, as per usual.
We then applied science to several data sets.
At this point, let's remind ourselves that there are so many combinations of things to try in this
cause.
For example, there are other data sets you could have tried.
You can add more layers, you can take away layers, you can use flatten instead of global max pooling.
You can use technique activations instead of real use for stock returns.
You can add different features, for example, those based on technical indicators.
So it really the possibilities are endless.
You've been taught all the code you need to know, but if you have any ideas, you want to try using
that code, please do so and share your results on the Q&A.