singlehiddenlayer

A single hidden layer neural network is a type of artificial neural network with one layer of artificial neurons between the input and output layers. This intermediary layer is known as the hidden layer because its outputs are not directly observed. The neurons in the hidden layer receive inputs from the input layer, process them using activation functions, and then pass the results to the output layer.

The architecture of a single hidden layer network consists of an input layer, a single hidden layer, and an output layer. The number of neurons in each layer can vary. The input layer's size is determined by the number of features in the data. The output layer's size depends on the nature of the task, such as one neuron for binary classification or multiple neurons for multi-class classification. The number of neurons in the hidden layer is a hyperparameter that needs to be tuned.

These networks are capable of learning complex, non-linear relationships between inputs and outputs, making them suitable for a variety of tasks like pattern recognition and function approximation. A significant advantage is their relative simplicity compared to deep neural networks, which can lead to faster training times and less computational resources. However, for highly complex problems, a single hidden layer might not possess sufficient capacity to model the underlying patterns effectively, potentially leading to underfitting. The choice of activation function within the hidden layer, such as ReLU or sigmoid, plays a crucial role in the network's ability to learn these non-linearities.