What are the differences between MLP and DNN? 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:
&quot;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 &quot;fed forward&quot;, 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.&quot;
So what makes MLP different from DNN ?
You explained it already quite well. An MLP is a type of neural network, the same way CNNs, RNNs, and other types exist. DNN is an umbrella term for all types of neural networks.
The reason some literature may be using these terms interchangeably is likely due to the fact that MLPs are some of the oldest forms of neural networks and therefore, at the time the literature was written, MLPs and DNNs were ubiquitous.