What’s the Difference Between the Cell and Hidden State in LSTM?

Answer: In an LSTM (Long Short-Term Memory) network, the cell state represents the memory of the network, storing information over time, while the hidden state contains the information that is passed to the next time step.

Here’s the difference between the cell state and hidden state in LSTM presented in a tabular form:

Aspect	Cell State (Ct)	Hidden State (ht)
Function	Maintains long-term memory, and stores information over time.	Captures short-term dependencies, and reflects the current state of the network.
Memory	Represents the memory of the LSTM, retaining information across long sequences.	Carries information passed to the next time step, influencing network predictions.
Influence on Output	Indirectly affects the hidden state through gate operations.	Directly contributes to the hidden state, which is the output of the LSTM cell.
Persistence	Preserves information across multiple time steps.	Reflects the current state of the network’s learning process at each time step.
Importance	Crucial for addressing the vanishing gradient problem and retaining relevant information.	Essential for capturing short-term patterns and making accurate predictions.

Conclusion:

This tabular representation succinctly outlines the distinctions between the cell state and hidden state in LSTM networks, emphasizing their respective functions, memory retention properties, influence on network output, and overall importance in the learning process.