Hidden layers are pivotal components of neural networks, situated between the input and output layers, performing nonlinear transformations of the inputs into something that the output layer can use. Each neuron in these layers applies a set of weights to the inputs, adds a bias, and typically passes the result through a nonlinear activation function. This allows the network to learn complex patterns and relationships within the data, facilitating tasks such as image and speech recognition, natural language processing, and more. The architecture, including the number and size of hidden layers, significantly influences the network's capacity to model complex functions and generalize from the data.
Historical overview: The concept of hidden layers has been part of neural network architectures since their resurgence in the 1980s, particularly with the popularization of backpropagation algorithms which made training multi-layer networks feasible. Initially theorized in models from the 1960s, practical applications and widespread implementation were not realized until computational resources caught up decades later.
Key contributors: While the foundational ideas date back to early neural network models proposed by pioneers like Frank Rosenblatt, the modern understanding and application of hidden layers owe significantly to researchers such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio. These contributors, among others, played crucial roles in the development of deep learning techniques that effectively utilize multiple hidden layers for complex problem-solving in AI.