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Vocabulary

Understand the term. Connect the concept.

A practical dictionary of 512 AI, machine learning, mathematics, and programming terms from HerbDev’s AI Vocabulary app. Start with a definition, explore an example, then follow the concepts behind it.

Linked concepts open in a new tab. Audio reads the definition and example.

LSTM

RNNs · Level 3 of 5

LSTM (LSTM)

A recurrent architecture with gates and a cell state designed to preserve useful information.

Gates regulate writing, retaining, and exposing state.

Example

A sequence model learns to keep a signal across many time steps.

Listen to the definition and example

Audio transcript

LSTM. A recurrent architecture with gates and a cell state designed to preserve useful information. Gates regulate writing, retaining, and exposing state. For example: A sequence model learns to keep a signal across many time steps.

Explore this concept

A useful analogy

A notebook with separate controls for retaining, writing, and revealing information.

Why it matters

This helps you trace information and gradient flow through sequence models.

Technical detail

Gated elementwise operations update a cell state, typically c_t = f_t⊙c_(t−1)+i_t⊙g_t, and h_t = o_t⊙tanh(c_t).

Common misconception

Gates help preserve information but do not guarantee unlimited long-term memory.

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Related concepts

Quick recall question

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Put the terminology into context: How AI works · Machine-native representations · All learning paths

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