In this video, we are going to summarize everything we learned in the section and perhaps give you
a few things to think about, this section focused on recurrent neural networks.
As usual, we first learned about the theory behind Arnon's and then we use them in code four time series
applications.
One of the nice features of tens of flowing cameras is that they make working with Arnon's very, very
easy.
A lot of you new students won't realize this, but Arnon's are actually pretty complicated to build,
much more so than Anand's or cornets.
So if you ever took my original course on Arnon's, you've seen this for yourself.
Basically, it's because of the fact that when you're working with a static computation graph, you
cannot use for loops, at least not the kind you typically think of.
And of course, for loops are kind of what you need in order to iterate over a sequence of inputs.
In the old days, we use the YANNO, which was the original deep learning library in Python.
And so if you ever built in Ahren and at V.A., then you've experienced what I'm referring to.
Now, luckily, in modern times, you can still get that same experience if you like.
It turns out that Google has not one but two deep learning libraries.
One is obviously tensor flow, but the other is called Jacques's, which has been quite popular internally.
So Jacques's is a lower level library, meaning that you typically implement things from scratch, the
same as you wouldn't theno.
And because of this very similar programming, constructs are used when you build in Arnet and Jaxx.
So it's kind of an interesting aside on how you might get the full or an end experience.
Now, what I hope you got out of this section is that it made you think think about what assumptions
people make when it comes to our own ends.
I see a lot of students get really excited about Arnon's and Steam's, but keep in mind that Time series
are not where the LSM proved itself.
In fact, it appears to me that some students get away more excited about ellis' times than they should.
I've seen many instances where CNN's outperform Ms.
So what's the lesson here?
Well, the lesson is that don't use something simply because you think it sounds cool.
Instead, use what works.
Of course, the only way to find out whether or not something will work is to run experiments and to
try those things yourself.
That said, what we did in this section does count as running experiments.
So add what you've observed here to your list of experience and use that to guide your future decisions
about whether or not you will choose to use the MS.