Hidden layer: Difference between revisions

Hidden layers enable neural networks to learn and represent complex relationships between input and output data. In essence, hidden layers act as feature detectors, recognizing important aspects of input data that influence prediction accuracy. Each hidden layer in a neural network can learn to represent higher-level features of input data based on the lower-level features learned in previous layers.

Image recognition tasks often begin with multiple hidden layers that learn to detect edges and corners in an image, while the second hidden layer learns more complex shapes like circles or rectangles. Finally, the final output layer uses these features to make a prediction about what object is present in the picture.

Without hidden layers, a neural network would only be capable of capturing linear relationships between input and output data, not being able to recognize complex patterns present within it.