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Congrats on making it to
the end of this course,

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where you've learned
a lot about how to

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implement models for
natural language processing.

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But the sequence models
you've learned like the RNN,

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the GIU, the LCM,

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they're useful for
even more applications

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specifically time-series
applications.

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Everything from
processing, whether

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data to processing
stock market data,

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to try and understand

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"EKG" electrocardiogram that's

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time series electric
recordings of your heart.

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These types of models are useful,

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all of these applications.

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So in the next course,

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you go deeper to learn more about

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how to build and
train such models.

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So like a lot of that stuff
as Andrew has mentioned just

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that we've done with
natural language processing

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and sequence modeling,

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and even other things that we've

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learned in this course
for example,

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convolutions and the convolutional
neural network course,

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are all going to be able
to come together as you

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start doing sequences
and prediction,

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and we think it's
going to be a really,

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really nice module to help really

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just build under
those skills that

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you've been doing and help you

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move towards mastery
of in-terms of them.

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These models are important
and in the next course,

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I hope you enjoy
learning about them.

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So please go on to
the next course.