RNNkerroksia

RNNkerroksia, which translates to "RNN layers" in Finnish, refers to the fundamental building blocks of Recurrent Neural Networks (RNNs). RNNs are a type of artificial neural network designed to process sequential data, such as text, speech, or time series. The "kerros" or layer in an RNN is responsible for processing the input at a specific time step and maintaining a hidden state that carries information from previous steps.

A standard RNN layer typically involves a set of weights and biases that are applied to the current input and the previous hidden state. The output of this operation is then passed through an activation function to produce the new hidden state and, optionally, an output for the current time step. This recurrent connection, where the output of a layer influences its subsequent input, is what allows RNNs to capture temporal dependencies.

More complex RNN architectures often employ variations or combinations of these basic layers. For instance, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are sophisticated types of RNN layers that use gating mechanisms to better control the flow of information and mitigate the vanishing gradient problem, enabling them to learn long-range dependencies more effectively. Understanding the concept of RNN layers is crucial for comprehending how these networks learn and operate on sequential data.