So I have been reading about the topic for a while, but i did not find a clear answer why MLP and DNN are being used interchangeably even though there are some differences between them.
So far I have filtered some informations:
"The field of artificial neural networks is often just called neural networks or multi-layer perceptrons after perhaps the most useful type of neural network. A perceptron is a single neuron(input, output, weights, activation) model that was a precursor to larger neural networks.
MLP is a subset of DNN. While DNN can have loops and MLP are always feed-forward(a type of Neural Network architecture where the connections are "fed forward", do not form cycles (like in recurrent nets). Multilayer Perceptron is a finite acyclic graph, not like RNN and it's subsets which are cyclic in nature. MLP uses back propagation for training the network."
So what makes MLP different from DNN ?