Multilayer Perceptron in Machine Learning

Rupika Nimbalkar
appengine.ai
Published in
2 min readOct 13, 2021

Multilayer Perceptron is an extremely useful technique for research in Machine Learning.

Multilayer Perceptron in Machine Learning also known as -MLP. It is widely known as a feedforward Artificial Neural Network. When Multilayer Perceptrons have a single-layer neural network they are informally also referred to as vanilla neural networks. They are commonly used to solve simple regression problems as it helps us to get a sneak pic about underlying reasons in the ultra-modern models of deep learning and also obtain information about it. It also requires a large number of parameters to operate multi-dimensional data, due to it is efforts in remembering the patterns of data in chronological order. They are extremely useful for categorizing data sets as linearly separable. Actually, when combined with classification and regression problems they deliver amazing results. Hence it has become a profitable deal for AI-Startups to leverage themselves with this technique. They are used in applications that require supervised learning and a lot of research in computational neuroscience. That's why they are helpful in speech recognition, image recognition, and machine translation.

Working of Multilayer Perceptron

The Multilayer Perceptron consists of an input layer and an output which are interconnected with each with the help of a number of hidden layers that are located in between them. Here excluding the input nodes, the rest other nodes are the neuron that uses nonlinear activation function. For training the purpose the technique which is used is supervised learning known as backpropagation. Once the input layers receive the signal it is to have proceeded. Then the hidden layer comes into action which is actually the computational engine of this technique. Here the data flows in the forward direction from input to output layer.

Conclusion

Hence we can conclude that this is an extremely popular machine learning technique that is quite beneficial in applications like speech recognition, image recognition, and machine translation software.

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