Feedforward Neural Network
A feedforward neural network is a type of artificial neural network where information moves in only one direction—forward—from the input layer, through hidden layers, to the output layer. There are no loops or feedback connections, distinguishing it from recurrent neural networks.
Historical context
Feedforward architectures are the foundation of modern deep learning. The perceptron, introduced in 1958, is the simplest feedforward model. Later developments such as multi-layer perceptrons (MLPs) enabled nonlinear transformations, which significantly expanded their capabilities.
Use cases
- Image recognition (basic tasks): e.g., digit classification in MNIST.
- Regression problems: mapping continuous inputs to outputs.
- Early speech recognition and recommendation systems.
Strengths & weaknesses
- Strength: relatively easy to design and train compared to more complex architectures.
- Weakness: lacks temporal memory—cannot handle sequential dependencies or contextual information.
- Today, FFNNs are often embedded as components within larger architectures such as transformers or CNNs.
Feedforward neural networks are sometimes called the “vanilla” form of neural networks, since they represent the most straightforward flow of information. Each layer applies a mathematical transformation to the input, gradually mapping raw features into useful predictions.
One of the key insights of FFNNs is the universal approximation theorem, which states that a network with at least one hidden layer and non-linear activations can approximate any continuous function, given enough neurons. This result highlighted their theoretical power, even if in practice deeper or more specialised architectures often perform better.
Although FFNNs are no longer at the cutting edge for tasks like vision or language, they remain essential in structured data problems—for example, predicting customer churn, assessing credit risk, or modeling tabular datasets. Their relative simplicity makes them interpretable and efficient, which explains why they are still widely used in applied machine learning.
Further reading
- Nielsen, M. Neural Networks and Deep Learning (2015).
