Part of the vision of TensorFlow
to make machine learning and
deep learning easier to learn and easier to use,
is the concept of having built-in data sets.
You seen the little bit of a preview of
the way back in the first course,
when the fashion MNEs was available to you without you
needing to download and split
the data into training a test sets.
Expanding on this, there's a library called
TensorFlow Data Services or TFTS for short,
and that contains many data sets
and lots of different categories.
Here's some examples; and while we
can see that there are many
different data sets for different types,
particularly image-based, there's also a few for text,
and we'll be using the IMDB reviews dataset next.
This dataset is ideal
because it contains a large body of texts,
50,000 movie reviews which
are categorized as positive or negative.
It was authored by Andrew Mass et al at Stanford,
and you can learn more about it at this link.