Congratulations on coming to
the end of this second course.
Just cause you've learned how to build bigger confidence,
how to implement data augmentation,
how to implement transfer learning
as well also how to use
multi-class cross size but
there's still a lot more to learn.
Lot's the examples you've seen so
far have used many computer vision.
In the next course, you'll learn how to deal with
natural language processing to how to work with texts.
It's going to be a lot of fun switching
gears as well from dealing with
pixels to dealing with characters and dealing with words.
We'll take a look at how to tokenize words,
how to generate embeddings,
so that we can learn off of embeddings.
An embedding is where we can turn
a word into basically a vector in
a multi-dimensional space and
from the direction that vector
points in we can start ascertaining
the semantics of that word.
We'll be going into all of that and how
words also work in sequence and
different sequence models for learning what
the context of a sentences and what
the semantics of the sentence is, yeah.
That sounds exciting. So I think
natural language processing is really
taking off partly because of deep learning.
So in the next course,
you'll learn a lot about that and
give that built some of these exciting models yourself.
So let's go to the next course.