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.