ML//RNN//hidden state

The fixed-size vector an RNN carries from one timestep to the next: a running summary of everything seen so far.

The fixed-size vector an RNN carries from one timestep to the next: a running summary of everything seen so far.

Updated each step from the new input and the previous hidden state: h_t = tanh(W_x x_t + W_h h_{t-1} + b).

In an LSTM it is the working output, filtered from the private cell state by the output gate (distinct from the cell state, which is the long-term ledger).

A prediction never comes from the hidden state directly; a separate readout layer reads it.