Congrats on making it to the end of this course,
where you've learned a lot about how to
implement models for natural language processing.
But the sequence models you've learned like the RNN,
the GIU, the LCM,
they're useful for even more applications
specifically time-series applications.
Everything from processing, whether
data to processing stock market data,
to try and understand
"EKG" electrocardiogram that's
time series electric recordings of your heart.
These types of models are useful,
all of these applications.
So in the next course,
you go deeper to learn more about
how to build and train such models.
So like a lot of that stuff as Andrew has mentioned just
that we've done with natural language processing
and sequence modeling,
and even other things that we've
learned in this course for example,
convolutions and the convolutional neural network course,
are all going to be able to come together as you
start doing sequences and prediction,
and we think it's going to be a really,
really nice module to help really
just build under those skills that
you've been doing and help you
move towards mastery of in-terms of them.
These models are important and in the next course,
I hope you enjoy learning about them.
So please go on to the next course.