FCN Meaning and
Definition
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FCN is an abbreviation that stands for "Fully Connected Network." A Fully Connected Network is a specific type of artificial neural network architecture used in machine learning and deep learning. It is also referred to as a dense network or multi-layer perceptron (MLP).
In an FCN, each neuron in a particular layer is connected to every neuron in the subsequent layer, forming a fully connected graph. This means that the neurons in one layer receive inputs from all neurons in the previous layer and transmit outputs to all neurons in the subsequent layer. Consequently, the connections between the neurons enable information propagation throughout the network.
The primary purpose of FCNs is to transform a set of input features into a corresponding set of output values. Typically, the FCN consists of an input layer (which accepts the initial input), one or more hidden layers, and an output layer (which produces the final output). Each neuron within the FCN performs a weighted sum of its inputs, followed by the application of an activation function that introduces non-linearity to the network.
FCNs are capable of capturing complex patterns and relationships in the input data due to their fully connected structure, allowing them to handle a wide range of tasks, such as classification, regression, and feature extraction. Despite being prone to overfitting in some cases, FCNs have been widely used and have proven successful in various fields, including computer vision, natural language processing, and speech recognition.
Common Misspellings for FCN
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