Cacherétegek

Cacherétegek is a nonstandard or informal Hungarian term sometimes used to refer to the hidden layers of artificial neural networks. The word combines the French caché, meaning hidden, with the Hungarian word rétegek, meaning layers. In standard Hungarian, the common term is rejtett rétegek. The concept also appears as “hidden layers” in English-language texts.

Hidden layers are the intermediate layers between the input layer and the output layer in a neural network. Each hidden layer applies a linear transformation followed by a nonlinear activation function, allowing the network to learn abstract and hierarchical representations of the input data. By stacking multiple hidden layers, a network can model complex patterns and functions that are difficult to capture with a single layer.

Training and design considerations for hidden layers include the number of layers (depth), the number of neurons per layer (width), and the choice of activation functions. These choices influence the network’s capacity, expressiveness, and tendency to overfit. Training typically uses supervised learning with backpropagation and gradient-based optimization. Challenges in deeper networks include vanishing and exploding gradients, which have driven developments such as residual connections, normalization techniques, and improved optimizers.

Hidden layers are central to a wide range of architectures, including feedforward networks, convolutional neural networks for image processing, recurrent networks for sequence data, and transformer-based models. They are not directly observable in inputs or outputs but enable the learned representations that drive predictive performance.