Feed forward Networks

Feedforward networks are a crucial part of neural network architecture, where information moves in only one direction – from input to output without any loops or cycles. Think of them as assembly lines where data is progressively processed through successive layers.

These networks consist of multiple layers that are fully connected, meaning each "neuron" in one layer is connected to every neuron in the next layer. This allows the network to learn intricate patterns and relationships in the data.

Each layer performs a mathematical calculation that involves multiplying the input by a set of weights, adding a bias, and then applying a special function called an activation function. This activation function introduces non-linearity, which is essential for the network to learn complex patterns that aren't just straight lines.

In transformer architectures, feedforward networks act as mini-brains that process the contextualized information from the self-attention mechanism. This is because each layer’s self-attention mechanism first captures inter-dependencies, then the feedforward networks independently apply non-linear transformations to extract higher-level features.