A feedforward neural network (FNN) is a type of artificial neural network where information flows in one direction, from the input layer through hidden layers (if any) to the output layer, without cycles or loops. This structure distinguishes it from recurrent neural networks (RNNs), which allow feedback connections. In an FNN, each layer consists of nodes (neurons) that apply mathematical operations to the data. The input layer receives raw data, such as pixel values from an image or features from a dataset, and each subsequent layer processes this data using weighted connections and activation functions. For example, in a simple FNN for image classification, the input might represent pixel intensities, hidden layers could detect edges or textures, and the output layer might predict the image’s class (e.g., “cat” or “dog”).
The network’s architecture is defined by its layers and the connections between them. Each neuron in a layer is connected to every neuron in the next layer, forming a “fully connected” structure. During training, the network adjusts the weights of these connections using optimization algorithms like gradient descent. This process minimizes a loss function, which measures the difference between the network’s predictions and the actual targets. For instance, if training a network to predict housing prices, the loss function might quantify how far the predicted prices are from the true values. Activation functions, such as ReLU (Rectified Linear Unit) or sigmoid, introduce non-linearity, enabling the network to model complex patterns. Without these functions, the network would only learn linear relationships, limiting its usefulness.
Feedforward neural networks are widely used for tasks like regression, classification, and pattern recognition due to their simplicity and effectiveness. They excel in scenarios where the input data has a fixed structure, such as tabular data or flattened images. However, they lack memory, making them unsuitable for sequential data like time series or natural language, where RNNs or transformers are better choices. A common example is using an FNN for the MNIST handwritten digit dataset: the input layer has 784 nodes (one per pixel), hidden layers might reduce dimensionality, and the output layer has 10 nodes (one per digit). While FNNs are foundational, their performance depends heavily on data preprocessing, layer design, and regularization techniques like dropout to prevent overfitting. Developers often start with FNNs as a baseline before exploring more complex architectures.