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Long short-term memory (LSTM) networks

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By Team EasyExamNotes

Long short-term memory (LSTM) networks are a type of recurrent neural network (RNN) architecture used in the fields of deep learning and machine learning.

Unlike feedforward neural networks, LSTMs have feedback connections that allow them to process entire data sequences. As a result, they are well-suited for tasks that require sequential data, such as natural language processing (NLP), speech recognition, and machine translation.

Core of LSTM:

LSTMs are a type of RNN that can learn long-range dependencies in sequences. This is because they have a special type of memory cell called an LSTM unit.

An LSTM unit has four parts:

  • A forget gate: This gate decides which information to forget from the previous state.
  • An input gate: This gate decides which new information to add to the current state.
  • An output gate: This gate decides which information to output from the current state.
  • A cell: This stores the current state of the LSTM unit.

By carefully controlling the flow of information through these gates, LSTMs can learn to remember information over long periods of time.

Applications of LSTM

  • Natural language processing (NLP): LSTMs are used in NLP tasks such as machine translation, text summarization, and question answering.
  • Speech recognition: LSTMs are used in speech recognition systems to convert spoken language into text.
  • Machine translation: LSTMs are used in machine translation systems to translate text from one language to another.
  • Time series forecasting: LSTMs are used in time series forecasting tasks to predict future values of a time series, such as stock prices or sales figures.

Advantages of LSTM

  • They are less susceptible to the vanishing gradient problem. The vanishing gradient problem is a common challenge in training RNNs, which occurs when the gradients of the error function become very small during backpropagation. This makes it difficult for the network to learn. LSTMs are able to mitigate the vanishing gradient problem by using a gating mechanism that allows the gradients to flow through the network more effectively.
  • They can learn long-range dependencies in sequences. This makes them well-suited for tasks involving sequential data, such as natural language processing (NLP), speech recognition, and machine translation.

Disadvantages of LSTM

  • They are more complex to train than other types of RNNs. This is because they have more parameters to train.
  • They can be more computationally expensive to train than other types of RNNs. This is because they require more time to process sequences of data.
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