2layer

2layer refers to a two-layer neural network architecture. This is a fundamental type of artificial neural network that consists of an input layer, a single hidden layer, and an output layer. Information flows from the input layer, through the hidden layer where computations are performed, and finally to the output layer which produces the result.

The hidden layer is where the network learns to extract features from the input data. The number of neurons in the hidden layer is a hyperparameter that can be tuned. The connections between neurons in different layers have associated weights, which are adjusted during the training process through algorithms like backpropagation. These weights determine the strength of the signal passed between neurons.

Two-layer neural networks are capable of learning complex patterns and relationships within data, and are often used for tasks such as classification and regression. While more complex architectures like deep neural networks with multiple hidden layers exist, the two-layer network serves as a foundational building block and a starting point for understanding more advanced neural network concepts. The simplicity of its structure makes it computationally less intensive to train and can be effective for problems that do not require an extremely high degree of abstraction.