Last week, we looked at recurrent neural networks including a simple RNN and
an LSTM.
You saw how they could be useful in learning a sequence like the one that
we've been looking at.
And how the LSTMs removed some of the issues we had with an RNN.
This week, you'll go one step further,
combining convolutions with LSTMs to get a very nicely fitting model.
We'll then apply that to real world data instead of this
synthetic data set that we've been working on since the beginning of this course.
Here's the LSTM that you looked at last week,
except that I've added something at the beginning of the sequential stack.
It's a convo D where we'll try to learn 32 filters.
It's a one dimensional convolution.
So we'll take a five number window and multiply out the values in that
window by the filter values, in much the same way as image convolutions are done.
Check back to the convolutions course to learn more about that.