When processing sequential data with a standard Recurrent Neural Network (RNN), which of the following represents the primary training limitation that limits its ability to learn relationships across long sequences?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of a standard Recurrent Neural Network (RNN) like a person trying to read a massive novel but only having a memory that lasts for a few sentences. As the story goes on, the beginning of the book just fades away. In deep learning, we call this the vanishing gradient problem. When we run backpropagation through time, those gradients get multiplied over and over at each time step. If they're small numbers, they shrink down to absolutely nothing. By the time you get back to the start of the sequence, the network has zero signal left to adjust its weights. That means it completely forgets long-term dependencies! Option A is the answer you're looking for.
Full explanation below image
Full Explanation
Recurrent Neural Networks (RNNs) are designed to process sequential data by maintaining an internal hidden state that acts as memory. However, when trained using Backpropagation Through Time (BPTT) over long sequences, standard RNNs encounter a severe limitation known as the vanishing gradient problem. During the backward pass, the gradient of the loss function is propagated back through the temporal steps. This involves repeated matrix multiplications of the network's recurrent weight matrices. If the eigenvalues of these matrices are less than one, the gradients shrink exponentially as they propagate backward across many time steps. Consequently, the weight updates for the early steps in the sequence become negligible, meaning the network fails to learn long-term dependencies (relationships between inputs separated by large temporal distances). Let's examine why the other choices are incorrect: Option B is incorrect because standard RNNs are conceptually designed to process sequences of variable lengths. Option C is incorrect because RNNs do not require spatial feature maps from CNNs; they can process raw sequential vectors (e.g., text tokens or sensor data) directly. Option D is incorrect because RNNs are actually computationally intensive and prone to slow sequential execution, which does not inherently cause immediate overfitting. For the exam, remember that vanishing gradients are the core reason standard RNNs struggle with long-term dependencies, prompting the development of LSTMs and GRUs.