Feed-Forward Neural Network Layer

A Feed-Forward Neural Network Layer is a neural network layer in a feedforward neural network that processes input data forward without any cycles or loops.

• Context:
  • It can (typically) be found as one of many such layers in a Deep Neural Network where each layer's output serves as the next layer's input.
  • It can apply a weight matrix to its inputs followed by a bias addition and then an activation function to introduce non-linearity.
  • It can be used in both simple networks for tasks like regression and classification, and complex architectures like CNNs and Transformers for more advanced tasks such as image recognition and natural language processing.
  • It can vary in size (number of neurons) and function depending on its position within the network and the specific task it is designed to perform.
  • It can (often) be optimized through backpropagation and gradient descent methods during the training process to minimize the loss between predicted and actual outputs.
  • It can be connected in a variety of topologies, though in a standard feed-forward network, the connections are strictly unidirectional, from input to output.
  • It can benefit from techniques such as dropout and batch normalization to improve training efficiency and model generalization.
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• Example(s):
  • One of the layers in a Multilayer Perceptron (MLP) used for digit recognitions.
  • a Fully Connected Feed-Forward Neural Network Layer in a Convolutional Neural Network (CNN) that compiles the features extracted by the convolutional layers for classification.
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• Counter-Example(s):
  • A Recurrent Neural Network (RNN) layer, which processes data sequentially and maintains a hidden state that carries information across sequence elements.
  • A Convolutional Layer in a CNN, which applies a convolution operation and is primarily used for pattern recognition in spatial data like images.
• See: Activation Function, Backpropagation, Deep Learning, Artificial Neural Network.