Description: In the context of Recurrent Neural Networks (RNN), ‘YIELD’ refers to the output generated by the network based on the input sequence. RNNs are a type of neural network architecture designed to process sequential data, making them particularly suitable for tasks such as natural language processing, time series prediction, and speech recognition. Unlike traditional neural networks, which assume that inputs are independent of each other, RNNs have the ability to maintain information about previous inputs through their recurrent connections. This allows the network to ‘remember’ relevant information from the input sequence, which influences the generated output. The output or ‘YIELD’ can be a single value, a sequence of values, or even a probability distribution, depending on the specific task being addressed. The way ‘YIELD’ is calculated can vary depending on the architecture of the RNN, such as LSTMs (Long Short-Term Memory) or GRUs (Gated Recurrent Units), which are designed to mitigate issues like vanishing gradients and improve the network’s ability to learn long-term dependencies in sequential data.
