Carry state
A carry state is an LSTM’s persistent long-term memory representation, transported across sequence positions through selectively gated updates.
Sutskever's List
A cell state is the long-term memory pathway in an LSTM that carries information across time steps with selectively gated updates.
- How it is used
- Carries long-range information through LSTM sequences while gates regulate memory retention and exposure.
- How deep it goes
- Explains the carry pathway, its contrast with hidden state, and its three gating controls without deriving the update equations.
- Examples
- Uses the conveyor-belt analogy, long-sequence tasks, and standard and original LSTM cell schematics.
- What you can do after
- Understand how LSTMs preserve, update, and expose information across time steps.
Natural Language Processing in Action, Second Edition
A cell state is the persistent recurrent representation maintained by an LSTM across sequence positions.
- How it is used
- Carries long-term sequence information between LSTM time steps while the hidden state supplies each current output.
- How deep it goes
- Explains gate roles, state equations, and hidden-state interaction, but does not derive the learned weight matrices.
- Examples
- Uses token sequences, long documents, gate diagrams, and compact matrix equations to illustrate recurrent memory.
- What you can do after
- Understand how LSTMs preserve, update, and expose sequence information through a separate cell-state pathway.
Read how Natural Language Processing in Action, Second Edition explains carry state →
Deep Learning with R, Third Edition
A carry state is an information stream in an LSTM that transports a representation across sequence timesteps for later modification or use.
- How it is used
- Explains how LSTM cells preserve and update information across sequence timesteps.
- How deep it goes
- Descends to the LSTM carry equation and transformations, while stopping short of assigning fixed meanings to learned operations.
- Examples
- Uses an LSTM anatomy figure and pseudocode showing output, gate, and carry-state calculations.
- What you can do after
- Evaluate how an LSTM transports, forgets, and incorporates information through its carry pathway.
Read how Deep Learning with R, Third Edition explains carry state →
Deep Learning with Python, Third Edition
The LSTM internal state that transports information across timesteps to preserve long-term context.
- How it is used
- Preserves information across timesteps within an LSTM for modeling long-term sequence dependencies.
- How deep it goes
- Explains the carry update through input and forget gates, but stops short of implementation details.
- Examples
- Shows the carry track diagram and the gated pseudocode equation for updating c_t+1.
- What you can do after
- Evaluate/understand how LSTM gates retain, discard, and write information through the carry state.
Read how Deep Learning with Python, Third Edition explains carry state →
Inside Deep Learning: Math, Algorithms, Models
The LSTM memory state that carries longer-term information separately from the hidden state.
- How it is used
- Separates long-term information storage from short-term computation within an LSTM.
- How deep it goes
- Explains the state’s role and its three gates, stopping short of complete update equations or implementation code.
- Examples
- Uses forget, input, and output gates plus diagrams showing multiplication, addition, and state interaction.
- What you can do after
- Understand how LSTM gates preserve, discard, and expose longer-term information through the carry state.
Read how Inside Deep Learning: Math, Algorithms, Models explains carry state →
Related concepts
- LSTMpart of · An LSTM is a recurrent neural network architecture with gated memory mechanisms that control information flow and help retain or update information across sequences.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.