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Recurrent Neural Networks (RNN)
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How do long short-term memory (LSTM) networks address the training data requirements for applications in finance, particularly in predicting stock prices over time?

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A

LSTMs require less data due to their ability to remember information over long sequences

B

LSTMs operate on fixed-size datasets and do not require historical data

C

LSTMs eliminate the need for pre-processing training data

D

LSTMs do not use previous data points for predictions

Understanding the Answer

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An LSTM has a special cell that stores useful information over long sequences. Other options are incorrect because Many think LSTMs only use a fixed batch of data and ignore past prices; Some believe LSTMs can skip data cleaning.

Key Concepts

long short-term memory (LSTM)
training data requirements
applications in finance
Topic

Recurrent Neural Networks (RNN)

Difficulty

hard level question

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understand

Deep Dive: Recurrent Neural Networks (RNN)

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Definition
Definition

Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.

Topic Definition

Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.

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