LSTM (Long Short-Term Memory) helps to address the problem of vanishing gradient in Recurrent Neural Networks (RNNs).

Here's a step-by-step explanation:

1. RNNs suffer from the vanishing gradient problem, where the contribution of information decays geometrically over time. This makes it difficult for the RNN to learn and tune the parameters of the earlier layers.

2. LSTM is a type of RNN that includes a memory cell and gating mechanisms. This structure allows it to maintain information over long periods, effectively mitigating the vanishing gradient problem.

3. The memory cell stores the state of a unit for an arbitrary length of time, while the input, output, and forget gates control the flow of information into and out of the cell.

4. By controlling the flow of information, the LSTM can learn to keep important information and forget irrelevant data, making it more effective at learning from sequences of data.

5. Therefore, LSTM helps to address the vanishing gradient problem in RNNs, making it a popular choice for tasks that involve sequential data, such as time series prediction, natural language processing, and more.

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LSTM (Long Short-Term Memory) helps to address the problem of vanishing gradient in Recurrent Neural Networks (RNNs).

Here's a step-by-step explanation:

1. RNNs suffer from the vanishing gradient problem, where the contribution of information decays geometrically over time. This makes it difficult for the RNN to learn and tune the parameters of the earlier layers.

2. LSTM is a type of RNN that includes a memory cell and gating mechanisms. This structure allows it to maintain information over long periods, effectively mitigating the vanishing gradient problem.

3. The memory cell stores the state of a unit for an arbitrary length of time, while the input, output, and forget gates control the flow of information into and out of the cell.

4. By controlling the flow of information, the LSTM can learn to keep important information and forget irrelevant data, making it more effective at learning from sequences of data.

5. Therefore, LSTM helps to address the vanishing gradient problem in RNNs, making it a popular choice for tasks that involve sequential data, such as time series prediction, natural language processing, and more.

Knowee AI · Accepted Answer

LSTM (Long Short-Term Memory) helps to address the problem of vanishing gradient in Recurrent Neural Networks (RNNs).

Here's a step-by-step explanation:

1. RNNs suffer from the vanishing gradient problem, where the contribution of information decays geometrically over time. This makes it difficult for the RNN to learn and tune the parameters of the earlier layers.

2. LSTM is a type of RNN that includes a memory cell and gating mechanisms. This structure allows it to maintain information over long periods, effectively mitigating the vanishing gradient problem.

3. The memory cell stores the state of a unit for an arbitrary length of time, while the input, output, and forget gates control the flow of information into and out of the cell.

4. By controlling the flow of information, the LSTM can learn to keep important information and forget irrelevant data, making it more effective at learning from sequences of data.

5. Therefore, LSTM helps to address the vanishing gradient problem in RNNs, making it a popular choice for tasks that involve sequential data, such as time series prediction, natural language processing, and more.

Which problem in RNNs does LSTM help to address?High varianceVanishing gradientOverfittingAll of the options givenBias

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