Major Python. What's the Difference Between CNN and RNN?

Source:

https://www.telusinternational.com/articles/difference-between-cnn-and-rnn

Simply speaking:

CNN=no history. No story storage. No before sequence, no next sequence. Visual processing and object classification and detection of the current "photo".

RNN=the current sequence with "all" old sequences. Like a sentence structure to detect the whole meaning from words, word groups and entities (president, location, street name etc.) and old  sentences before. 

"What's the difference between CNN and RNN?

The main difference between a CNN and an RNN is the ability to process temporal information — data that comes in sequences, such as a sentence. Recurrent neural networks are designed for this very purpose, while convolutional neural networks are incapable of effectively interpreting temporal information. As a result, CNNs and RNNs are used for completely distinct purposes, and there are differences in the structures of the neural networks themselves to fit those different use cases.

CNNs employ filters within convolutional layers to transform data (more on that later), whereas RNNs are predictive, reusing activation functions from other data points in the sequence to generate the next output in a series."

"President Roosevelt was one of the most influential presidents in American history. However, Roosevelt Street in Manhattan was not named after him." 

But especially can we build recursive-CNN to analyse oline video movies data in real time? 

Can we?