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Multilayer Perceptrons

A type of feedforward neural network with fully connected neurons and nonlinear activation functions, used to distinguish non-linearly separable data.

Input layer

The initial layer of neurons in a multilayer perceptron that receives input data, with each neuron representing a feature or dimension of the input data.

Hidden layer

Layers of neurons between the input and output layers in a multilayer perceptron, where each neuron receives inputs from the previous layer and produces an output passed to the next layer.

Output layer

The final layer of neurons in a multilayer perceptron that produces the network's output, with the number of neurons depending on the task being performed.

Weights

Associated with connections between neurons in adjacent layers, determining the strength of the connection and learned during the training process.

Bias neurons

Neurons in each layer (except the input layer) providing a constant input to the next layer, adjusting the activation function and learned during training.

Activation function

Functions applied to the weighted sum of inputs in neurons, introducing nonlinearity into the network to learn complex patterns in the data.

Training with backpropagation

The method used to train multilayer perceptrons, computing gradients of a loss function to update parameters iteratively and minimize loss.