All about Multilayer Perceptrons (MLPs)
Introduction
In this article, we have today one of the deep learning algorithms, and it is one of those algorithms that have an excellent place to start identifying with deep learning. This algorithm belongs to the category of feedforward neural networks with multiple layers of perception that have many and multiple functions, which are the activation functions. This algorithm consists of a Fully connected input layer and output layer. They have the same number of input and output layers but may have multiple hidden layers and can be used to build speech recognition, image recognition, and machine translation programs.
What are the Multilayer Perceptrons (MLPs)?
The MLPs algorithm is known as an automatic neural network, as it was trained using the standard backpropagation algorithm, and they are supervised networks, so they need the desired response for training. They learn how to convert input data into the desired response so that it is widely used to classify patterns. A fully connected multilayer neural network is called a Multilayer Perceptron (MLP). It has 3 layers including 1 hidden layer. If it contains more than one hidden layer, it is called a deep ANN. MLP is a typical example of a feedforward artificial neural network.
How does a multi-layered perspective work?
The Perceptron consists of an input layer and an output layer that is fully connected. MLPs have the same input and output layers but may have multiple hidden layers between the above layers, as shown below.
The MLP algorithm has several advantages in the following points:
Relative to the sensor, the input is pushed forward through the MLP by taking the point product of the input with the weights located between the multi-input layer and the hidden layer (WH). This point product results in a value in the hidden layer. We don’t push this value forward the way we do futuristic.
MLPs use activation functions in each of their computed layers. There are several activation functions to discuss: corrected linear units (ReLU), sine function, tanh. Push the computed output into the current layer through any of these activation functions.
Once the computed output in the hidden layer has been pushed through the activation function, push it to the next layer in the MLP by taking the point product with the corresponding weights.
Repeat steps two and three until the resulting layer is reached.
In the output layer, the computations will either be used for the backpropagation algorithm that matches the activation function that has been defined for the MLP (in the case of training) or a decision will be made based on the output (in the case of testing).
Conclusion
We have talked about one of the deep learning algorithms, and in this article, I know that it is not enough yet, but at first, we can stop now at defining the algorithm, and then we will move next time to implement it in real data. I hope you benefit from this simple part. Enjoy!
Mohamed B Mahmoud.Data Scientist.
