So in this lecture, we will be summarizing everything we learned in this section.
This section was all about recurrent neural networks.
As with CNN's, this section was really split into two parts.
How Arnold's work and how to implement them in TensorFlow in terms of how organs work, we learned about
three different variants the Alman unit, the GRU and the Alice team.
In the past, the GRU was said to be on par with the LSHTM, but more recently the LSM has been shown
to outperform the GRU.
As such, are examples used the team by default, but you were encouraged to try other units as an exercise
in terms of code.
We looked at multiple examples.
The first example was a simple example, which was to classify text.
We did the same example with Anand's and CNN's.
We learned that this is called a many to one task, since the input has many tokens, while the target
only has one.
We then looked at many too many tasks, such as parts of speech tagging.
We observed that you could naively apply all the code we learned and get 99 percent accuracy.
However, I also told you why this is misleading and showed you how to do this task correctly.
Unfortunately, when you do write the code to do this correctly, TensorFlow is not as performance at
this stage.
Practically speaking, for the real world, it may be worth investing in PyTorch if you want to use
these techniques on a real world project.
Both have pros and cons, but if what you care about is speed, then that's always another option to
explore.