Demystifying Generative Models by Generating Passwords -- Part 2

Hello, once again this is the second part of the "Demystifying Generative Models" posts so if you haven't read Part 1 yet, I really urge you to do so here. In the previous post, we discussed the differences between discriminative and generative models, took a peek to the fascinating world of probabilities and used that knowledge to develop a working Naive Bayes that generates passwords for us. Now, we will change our methodologies a little bit and explore how Deep Learning can help us when probabilities fail. Assuming that you have the context knowledge, of part 1, I will jump straight to the point. The Multinomial Naive Bayes model that was developed was based on the assumption that each feature is independent of each other and it worked quite well! But let's change the problem formulation slightly and observe how it behaves.