A multilayer perceptron (MLP) is a class of feedforward artificial neural networks (ANN), but in which cases are MLP considered a deep learning method?
Yes. There are too many words that mean the same thing. A multi-layer perceptron is one example of what is called ``deep learning''. V!ce versa, the properties of multi-layer perceptrons (most significantly that they have a critical capacity and a stability/capacity curve) has implications for deep learning algorithms.
Dear Juan Pedro Carbonell-Rivera ,
An MLP uses backpropagation as a supervised learning technique. Since there are multiple layers of neurons, MLP is a deep learning technique. MLP is widely used for solving problems that require supervised learning as well as research into computational neuroscience and parallel distributed processing.
https://www.techopedia.com/definition/20879/multilayer-perceptron-mlp#:~:text=An%20MLP%20uses%20backpropagation%20as,neuroscience%20and%20parallel%20distributed%20processing.
Regards,
Shafagat
Dear Juan Pedro Carbonell-Rivera,
First of all, I agree with Stam Nicolis and Shafagat Mahmudova.
This is a question of terminology. Sometimes I see people refer to deep neural networks as "multi-layered perceptrons", why is this? A perceptron, I was taught, is a single layer classifier (or regressor) with a binary threshold output using a specific way of training the weights (not backpropagation). If the output of the perceptron doesn't match the target output, we add or subtract the input vector to the weights (depending on if the perceptron gave a false positive or a false negative). It's a quite primitive machine learning algorithm. The training procedure doesn't appear to generalize to a multi-layer case (at least not without modification). A deep neural network is trained via backpropagation which uses the chain rule to propagate gradients of the cost function back through all the weights of the network.
So, the question is. Is a "multi-layer perceptron" the same thing as a "deep neural network"? If so, why is this terminology used? It seems to be unnecessarily confusing. In addition, assuming the terminology is somewhat interchangeable, I've only seen the terminology "multi-layer perceptron" when referring to a feed-forward network made up of fully connected layers (no convolutional layers, or recurrent connections). How broad is this terminology? Would one use the term "multi-layered perceptron" when referring to, for example, Inception net? How about for a recurrent network using LSTM modules used in NLP?
Good luck and Happy Model Training !
Samawel JABALLI
The perceptron has N inputs and one output. A multilayer perceptron includes intermediate(``hidden'') layers and can be used to represent associations that aren't linearly separable-which a perceptron can't represent at all. The typical example is the XOR rule, that can't be represented by a perceptron without a hidden layer, but can be represented by a multi-layer perceptron with one hidden layer.
Deep learning involves organizing representations according to scale and, it turns out, that multi-layer perceptrons can be used to realize such representations. However what many people seem not to have understood is that whatever the network, there are associations it can represent and others that it can't, no matter how long the computer runs: Whether for the single-layer (cf. https://hal.archives-ouvertes.fr/jpa-00211064/document)
or the multi-layer perceptron (cf. Article First Order Phase Transitions in the Binary Perceptron
)there's a ``stability-capacity'' diagram that defines the associations that can and can't be represented.To enlarge the space of associations, it's necessary to change the network. One way this can be done is the ``tiling algorithm'' of Mézard and Nadal,Article Learning in feedforward layered networks: The tiling algorithm
This algorithm can construct the network-but the network isn't, necessarily, the ``smallest'' one.
Yes for sure, it is : https://towardsdatascience.com/multilayer-perceptron-explained-with-a-real-life-example-and-python-code-sentiment-analysis-cb408ee93141
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