Feed-Forward Neural Network Architecture

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A Feed-Forward Neural Network Architecture is a neural net architecture that can be mapped to a directed acyclic graph (in which connections between the units do not form a cycle and where signals move from the input nodes, through the hidden nodes (if any), and to the output nodes).



References

2023

  • GBard
    • Feed-forward neural networks (FFNNs) are a fundamental type of artificial neural network (ANN) characterized by information flow in one direction only - forward from the input layer to the output layer. This means there are no feedback loops within the network. Due to their simplicity and interpretability, FFNNs are widely used as building blocks for more complex deep learning architectures.
    • Network Structure: An FFNN typically consists of three types of layers:
      • Input Layer: This layer receives the raw data that the network will process.
      • Hidden Layers: These layers are responsible for performing the actual computations and learning complex relationships within the data. The number of hidden layers and the number of neurons in each layer influence the network's capacity and learning ability.
      • Output Layer: This layer generates the final output of the network, which could be a prediction, classification, or other desired result.
    • Information Flow:
      • Data enters the input layer.
      • Each neuron in the hidden layers receives weighted input from the previous layer, performs a weighted sum, and applies an activation function to generate its output.
      • This process repeats across all hidden layers until reaching the output layer.
      • The output layer performs a final activation function and generates the final output.
    • Activation Functions: Activation functions introduce non-linearity into the network, allowing it to learn and represent complex relationships. Common activation functions include:
      • Sigmoid: Outputs values between 0 and 1, often used for binary classification.
      • Tanh: Outputs values between -1 and 1, suitable for regression tasks.
      • ReLU: Outputs the maximum of 0 and the input, popular for its efficiency and ability to handle sparsity.

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