What is one major disadvantage of RNNs compared to some other machine learning models?

Recurrent Neural Networks (RNNs) are designed specifically to handle sequential data and maintain information over time, but one of their notable disadvantages is indeed the slow training speed. This slow performance is largely due to the architecture of RNNs, which processes data in sequences, allowing information from previous time steps to influence subsequent time steps. This inherent sequential nature can result in lengthy training times, particularly when working with long sequences or large datasets.

Additionally, RNNs often require backpropagation through time (BPTT) to update their weights, a process that can be computationally intensive. The extended sequences mean that the gradient needs to be calculated for multiple time steps, which can compound the computational resources and time required for training. Consequently, while RNNs are powerful for specific tasks, particularly those involving time-series or sequential data, their training inefficiency compared to other models (like feedforward neural networks) is a significant drawback in practice.

Other options discuss aspects like data requirements or classification types that do not pertain to RNNs' training efficiency, reinforcing the idea that the slowness in training is a key feature to consider when evaluating this model against others.