Hello and welcome back to the course on Deep Learning.
I hope you're as excited
about this section of the course on recurrent Neural Networks as I am.
We're slowly venturing into the very complex,
very forward looking and cutting edge
areas of deep learning, and this is going to be very, very fun.
So today we're going to talk about how are we going to approach this section,
which contains so many different complex topics, so many concepts
that we need to get our head around. All right.
So in this section we will learn,
first of all the idea behind a recurrent neural networks.
We'll see how they compare to the human brain will understand what makes them
unique and special as compared to regular artificial neural networks.
Then we'll talk about the vanishing gradient problem, something that has,
been a major roadblock in
or had been a major roadblock in the development and utilization
of recurrent neural networks, something that prevented them
from being what they are now.
And, then we will move on
to the solution that one of the most popular solutions
to the vanishing gradient problem, the long short term memory,
or Lstm neural networks.
And we'll talk about their architecture be very exciting tutorials.
One of my favorite topics and we will find out exactly how they work.
And what what that complex structure is inside
them will break it down into simple terms, and you will be able to walk away
with a pretty solid understanding of LSTMs.
And then we'll talk about the practical intuition.
So in that pre-history we will have a practical example of using LSTMs.
But in this practical intuition, tomorrow we'll look at some,
great examples posted by one of the researchers.
One of the, most well known researchers in the space.
And we'll understand even better, on an intuitive level, how LSTMs
actually work, how they think will be like neuroscientists trying to understand
what's going on in the brain of an Lstm is going to be very exciting as well.
And then at the end, we'll have an extra tutorial on Lstm variations.
Something special, something you don't really have
to take this tutorial, but it's very quick just to get you up to speed
on what other options of, LSTMs exist out there in the world?
What what other architectures, you might come across in your work.
So hopefully you're excited and ready to get started.
And I can't wait to see you on the next tutorial.
Until then, enjoy deep learning.