A Red de una capa, often referred to as a single-layer perceptron, is a foundational type of red neuronal artificial that consists of one layer of output nodes. These output nodes are directly connected to the input features without any hidden layers in between. This architecture is particularly simple and serves as a starting point for understanding more complex red neuronal diseños.
In a One-Layer Network, each input feature is assigned a weight, and the output is computed as a weighted sum of these inputs. An función de activación is then applied to this sum to produce the final output. The most common activation function used in such networks is the step function or the linear activation function, although others like the sigmoid function can also be employed depending on the context.
Las redes de una capa son capaces de realizar tareas de clasificación binaria, where they can separate data points into two distinct classes based on a linear decision boundary. However, their simplicity also limits their capability; they cannot efficiently model complex, non-linear relationships in data. For problems requiring the learning of intricate patterns, deeper architectures with multiple layers (multi-layer perceptrons) are typically used.
Despite their limitations, One-Layer Networks are valuable in educational contexts to illustrate the basic principles of neural networks, including concepts of weights, biases, and funciones de activación. They also form the basis for understanding more advanced models in deep learning.