Feedforward Neural Networks (FNNs)

Feedforward neural networks (FNNs)

Feedforward Neural Networks (FNNs) are the simplest type of artificial neural networks where the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any), and finally to the output nodes. There are no cycles or loops in the network; the output of any layer does not affect the same layer or the preceding layers. This straightforward flow of data makes FNNs easier to understand and implement compared to other neural network architectures.

An FNN typically consists of an input layer, one or more hidden layers, and an output layer. Each layer is composed of neurons (also known as nodes or units), and each neuron in one layer is connected to every neuron in the next layer through weighted connections. The neurons process the input they receive by applying a weighted sum followed by a non-linear activation function, such as the sigmoid or ReLU (Rectified Linear Unit) function. The activation function introduces non-linearity into the network, enabling it to learn complex patterns in the data.

During the training phase, the network adjusts its weights based on the difference between the predicted output and the actual output using a method called backpropagation coupled with an optimization algorithm like gradient descent. The goal is to minimize a loss function that quantifies the error in the network’s predictions. Despite their simplicity, FNNs are powerful tools for solving problems like classification, regression, and pattern recognition when the data relationships are straightforward.

For more insights into this topic, you can find the details here