A multi-layer perceptron (MLP) is a type of artificial neural network (ANN) that consists of three or more layers of interconnected nodes, or neurons. MLPs are often used for supervised learning problems such as classification and regression.
Here's how an MLP works:
Input layer: The input layer receives the input data, which is typically a vector of features.
Hidden layers: The hidden layers perform computations on the input data using weights and biases to produce outputs. Each neuron in a hidden layer receives input from several neurons in the previous layer, performs a weighted sum of those inputs, and then applies an activation function to produce its output.
Output layer: The output layer produces the final output, which can be a vector of class probabilities in the case of classification, or a scalar value in the case of regression.
Backpropagation: After the output is produced, the error between the predicted output and the true output is calculated. This error is then backpropagated through the network, adjusting the weights and biases in each neuron to minimize the error. This process is repeated many times, with the weights and biases updated in each iteration, until the error is minimized.
Activation functions: Activation functions are used to introduce nonlinearity into the network, allowing it to learn complex patterns in the data. Common activation functions include the rectified linear unit (ReLU), sigmoid, hyperbolic tangent, and softmax.
MLPs can have varying numbers of hidden layers, with each layer containing a varying number of neurons. The number of layers and neurons is typically determined through trial and error, as well as other hyperparameter tuning techniques.
Tags: Multi-layer Perceptron, MLP, Deep Learning, Neural Network, Hidden Layers, Backpropagation, Activation Function, Supervised Learning
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