What is the primary purpose of the hidden layer in an artificial neural network?

The primary purpose of the hidden layer in an artificial neural network is to process and transform inputs through the network's weights and activation functions. Hidden layers are crucial because they enable the network to learn complex patterns in the data by applying various transformations. Each hidden layer consists of multiple neurons that take inputs, apply weights, and use activation functions to produce output signals. This process allows the network to capture non-linear relationships and more abstract features of the data.

In contrast to the input and output layers, which serve specific roles (one for receiving data and the other for delivering predictions), hidden layers are where the actual learning occurs. By stacking multiple hidden layers, neural networks can increase their capacity to model intricate functions, thus enhancing their performance on a variety of tasks, such as image recognition, natural language processing, and more.

The other choices reference aspects that don't accurately represent the fundamental role of the hidden layer. For example, while the option linking it to memory storage is relevant in the context of recurrent neural networks, it does not apply to standard neural networks. Similarly, connecting the input directly to the output does not involve hidden layers and does not facilitate complex transformations. Lastly, while regularization is an important method for preventing overfitting in machine learning, it is