The main problem that arises when training Recurrent Neural Networks (RNNs) on long sequences is the issue of vanishing or exploding gradients.

Here's why:

1. **Vanishing gradients**: As the sequence length increases, the gradients that are back-propagated can become extremely small, essentially approaching zero. This is known as the vanishing gradients problem. When this happens, the weights of the network cannot be updated effectively, leading to poor performance.

2. **Exploding gradients**: On the other hand, the gradients can also become extremely large, or 'explode', which can cause the learning process to become unstable and the model to diverge.

These problems make it difficult for the RNN to learn and retain long-term dependencies in the data, which is a crucial requirement for many sequence prediction tasks.

The other options mentioned, such as underfitting, overfitting, and high bias, are general problems that can occur in any type of neural network, not just RNNs or specifically when training on long sequences.

Question

The main problem that arises when training Recurrent Neural Networks (RNNs) on long sequences is the issue of vanishing or exploding gradients.

Here's why:

1. **Vanishing gradients**: As the sequence length increases, the gradients that are back-propagated can become extremely small, essentially approaching zero. This is known as the vanishing gradients problem. When this happens, the weights of the network cannot be updated effectively, leading to poor performance.

2. **Exploding gradients**: On the other hand, the gradients can also become extremely large, or 'explode', which can cause the learning process to become unstable and the model to diverge.

These problems make it difficult for the RNN to learn and retain long-term dependencies in the data, which is a crucial requirement for many sequence prediction tasks.

The other options mentioned, such as underfitting, overfitting, and high bias, are general problems that can occur in any type of neural network, not just RNNs or specifically when training on long sequences.

Knowee AI · Accepted Answer

The main problem that arises when training Recurrent Neural Networks (RNNs) on long sequences is the issue of vanishing or exploding gradients.

Here's why:

1. **Vanishing gradients**: As the sequence length increases, the gradients that are back-propagated can become extremely small, essentially approaching zero. This is known as the vanishing gradients problem. When this happens, the weights of the network cannot be updated effectively, leading to poor performance.

2. **Exploding gradients**: On the other hand, the gradients can also become extremely large, or 'explode', which can cause the learning process to become unstable and the model to diverge.

These problems make it difficult for the RNN to learn and retain long-term dependencies in the data, which is a crucial requirement for many sequence prediction tasks.

The other options mentioned, such as underfitting, overfitting, and high bias, are general problems that can occur in any type of neural network, not just RNNs or specifically when training on long sequences.

Which problem arises when training RNNs on long sequences?All of the given optionsUnderfittingVanishing or exploding gradientsOverfittingHigh bias

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Which problem arises when training RNNs on long sequences?

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