Multilayer Perceptron (in Neural Networks)
Kolla Bhanu Prakash · 2024
Multilayer Perceptron (MLP) is a combination of multiple perceptron models. An MLP consists of at least three layers of perceptrons, that is, an input layer, a hidden layer, and an output layer, which are fully connected. MLPs. The MLP classifier uses an MLP algorithm that is trained based on backpropagation. Multilayer perceptron in training uses gradient descent in which the backpropagation is used to calculate the gradients. Multilayer perceptron is also used for multi-class classification using softmax activation function. Backpropagation is used in the training of the neural network finetuning the weights. The two types of backpropagation are as follows: static backpropagation; and recurrent backpropagation. This technique is sufficiently general to operate on a variety of network designs, including fully connected networks, generative adversarial networks, and convolutional neural networks. The gradient descent algorithm's only relevant parameters, such as learning rate, are used in the process.