What are two limitations of recurrent neural network?

Disadvantages of Recurrent Neural Network

• Gradient vanishing and exploding problems.
• Training an RNN is a very difficult task.
• It cannot process very long sequences if using tanh or relu as an activation function.

How do you deal with Overfitting and Underfitting?

How to Prevent Overfitting or Underfitting

1. Cross-validation:
2. Train with more data.
3. Data augmentation.
4. Reduce Complexity or Data Simplification.
5. Ensembling.
6. Early Stopping.
7. You need to add regularization in case of Linear and SVM models.
8. In decision tree models you can reduce the maximum depth.

How do you solve an exploding gradient problem?

How to Fix Exploding Gradients?

1. Re-Design the Network Model. In deep neural networks, exploding gradients may be addressed by redesigning the network to have fewer layers.
2. Use Long Short-Term Memory Networks.
3. Use Gradient Clipping.
4. Use Weight Regularization.

How do you resolve the problem of vanishing exploding gradient in deep Nursen architectures also discuss vanishing exploding gradient problem in detail?

One simple solution for dealing with vanishing gradient is the identity RNN architecture; where the network weights are initialized to the identity matrix and the activation functions are all set to ReLU and this ends up encouraging the network computations to stay close to the identity function.

What are recurrent neural networks (RNN)?

What are recurrent neural networks? A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.

What is the vanishing gradient problem in recurrent neural networks?

For the vanishing gradient problem, the further you go through the network, the lower your gradient is and the harder it is to train the weights, which has a domino effect on all of the further weights throughout the network. That was the main roadblock to using Recurrent Neural Networks.

What happens when the WREC of a neural network is zero?

But, when you start with wrec close to zero and multiply xt, xt-1, xt-2, xt-3, … by this value, your gradient becomes less and less with each multiplication. What does this mean for the network? The lower the gradient is, the harder it is for the network to update the weights and the longer it takes to get to the final result.

What is the difference between recurrent neural networks and LSTMs?

All recurrent neural networks are in the form of a chain of repeating modules of a neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. LSTMs also have a chain-like structure, but the repeating module is a bit different structure.