What is the main advantage of LSTM over basic RNN?More layersNone of the given optionsHandling long-term dependenciesLower computational costFaster computation

Main Advantage of LSTM Over Basic RNN

The Long Short-Term Memory (LSTM) architecture is specifically designed to overcome the shortcomings of basic Recurrent Neural Networks (RNNs), particularly in handling long-term dependencies in sequential data. Basic RNNs struggle with this because they tend to forget information over long sequences due to the vanishing gradient problem, where gradients diminish as they are propagated backward through many layers.

1. Handling Long-Term Dependencies:

   • LSTMs introduce memory cells and various gates (input, output, and forget gates) that regulate the flow of information, allowing them to remember relevant information for long periods. This makes LSTMs significantly better at capturing dependencies over long sequences compared to basic RNNs.

2. None of the given options:

   • This does not apply as the handling of long-term dependencies is indeed one of the key advantages.

3. Lower Computational Cost:

   • This is not a primary advantage, as LSTMs are generally more computationally complex compared to basic RNNs due to their additional gates and memory cells.

4. Faster Computation:

   • While LSTMs may optimize certain computations relative to the problems they solve, they are typically not faster than basic RNNs in terms of raw computational speed.

5. More Layers:

   • The possibility of stacking more layers is not exclusive to LSTMs and can be implemented in basic RNNs as well.

Final Answer

The main advantage of LSTM over basic RNN is Handling long-term dependencies.