OK, let's see another application, very famous application of recurrent neural networks, sometimes
known as image captioning.
So the problem really is given an image, explain the image with with text or summarize the image,
but with the text.
So give it an image.
Just write the description of the image in text.
So the input really is a single image and the output is maybe a sentence, maybe a paragraph, maybe
a document or maybe a book depends upon what kind of image that is.
In this case, the input stays always of of the same length because it's a single image.
The in the individual elements of the image, they're not changing.
But if you see output at each time, so for example, the the caption or the output of the very first
image has one, two, three, four or five, six words.
And if you use full stops also it has basically seven tokens.
However, the output of the second image, this one, it has only one, two, three, four, five,
six and seven.
It also has seven words.
One, two, three, four, five, six, seven.
All these are seven.
One, two, three, four, five, six, seven, eight, nine.
So, for example, here we have nine words and maybe four other images.
We have more than nine or less than six or whatever number of words here, the lengths of the targets
in the training data, they are different.
For example, um, if we if you really want to generate a caption for an image, we have to build a
model, having some training data where we have images and the corresponding true captions and in the
training data, although the images they all have may have the same format or can be converted to a
same format and sizes.
But the captions, the outputs, they have different lengths in terms of the number of words in each
caption.
And in these cases also we're for each input.
The target length is different.
The Orendain is again the ideal situation.
The that's the ideal model to choose for these kind of applications.
They are a couple of more very famous applications of recurrent neural networks.
Let's just go through those before actually diving into the details of recurrent neural network.