Markov vs Feedforward

$\frac{1}{3}$ points $($graded$)$

What are some advantages of the feedforward NN as described in the lecture versus Markov models? $($Choose

all that apply.$)$

They contain a fewer number of parameters

We can easily control the complexity of feedforward NN by introducing hidden layers

They are able to encode more complex transition probabilities than Markov Models.

Suppose you have a word vocabulary of size $10($including