So in this lecture, we'll be summarizing everything we learned in this section on CNN's.
This section consisted of two main parts.
The first part discussed how convolution works in how to incorporate it into a neural network.
The second part looked at how to use CNN's in TensorFlow.
You learn that CNN's are nothing but a series of convolutions, followed by pooling followed by an end.
You'll learn that convolution acts as a pattern finder and that convolutional layers are much more efficient
than dense layers because they use shared weights.
This will be the case for Arden's as well.
This may surprise you, but what you have just learned is quite powerful.
Most courses do not look at how to use CNN's raw text, but in many cases CNN's have been shown to be
more powerful and more performant.
Then it ends in the field of NLP.
In fact, historically speaking, Convolution has played a huge role in time series analysis and signal
processing, as you recall.
Once we convert text into vectors, what we really have is a multidimensional time series and thus convolution
seems to be a very appropriate choice.