Recurrent neural networks are great for tasks involving sequential data. However, they do come with their drawbacks. This section will highlight and discuss one such drawback, known as the vanishing gradient problem.
The name vanishing gradient problem stems from the fact that, during the backpropagation step, some of the gradients vanish or become zero. Technically, this means that there is no error term being propagated backward during the backward pass of the network. This becomes a problem when the network gets deeper and more complex.
This section will describe how the vanishing gradient problem occurs in recurrent neural networks:
- While using backpropagation, the network first calculates the error, which is nothing but the model output subtracted from the actual output squared (such as the square error).
- Using this error, the model then computes the change in error with respect to the change in weights (de/dw).
- The...