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Long Short-Term Memory (LSTM) MCQ
10 Conceptual Questions with In-Depth Technical Explanations
Dissect forget gates, input gates, candidate cell state, output gates, and avoiding vanishing gradients.
Long Short-Term Memory (LSTM) Questions
Select an answer to reveal detailed technical explanation and formulas.
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What is the function of the 'Forget Gate' (f_t) in an LSTM cell?
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What is the role of the 'Input Gate' (i_t) and candidate cell state (C_tilde_t) in an LSTM?
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What is the exact update equation for the LSTM Cell State C_t?
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How is the output hidden state h_t generated from cell state C_t in an LSTM?
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Why are sigmoid functions used for LSTM gates while tanh is used for candidate state values?
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What are 'Peephole Connections' in an LSTM (Gers & Schmidhuber, 2000)?
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How many separate weight matrices (or gate computations) are executed per time step in a standard LSTM cell?
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What is the recommended initialization for the bias of the Forget Gate (b_f) in modern practice (Jozefowicz et al., 2015)?
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What is a major limitation of LSTMs compared to modern Transformer architectures?
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About Long Short-Term Memory (LSTM) Interview Questions & Quiz
These Long Short-Term Memory (LSTM) multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.
Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Long Short-Term Memory (LSTM).