Long Short-Term Memory (LSTM) Network

The Long Short-Term Memory (LSTM) network is a specialized type of recurrent neural network (RNN) that overcomes the vanishing gradient problem commonly faced in traditional RNN training. Its architecture incorporates a sophisticated memory cell mechanism controlled by three types of gates: input gates regulate information flow into the cell, forget gates determine which information to discard from previous states, and output gates control the information flow to the next time step. In practical implementation, LSTM networks typically use sigmoid activation functions for gate controls (producing values between 0 and 1) and tanh activations for candidate value transformations. The core computations involve matrix operations between input vectors and weight matrices, with element-wise multiplications for gate operations. This gating mechanism enables selective memory retention across extended time sequences, making LSTMs particularly effective for applications requiring long-term dependency modeling such as speech recognition (where context windows span multiple seconds), language translation (handling sentence structure dependencies), and video analysis (temporal pattern recognition across frames). Key functions in LSTM implementation include the cell state update equation (c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t) and hidden state calculation (h_t = o_t ⊙ tanh(c_t)), where ⊙ represents element-wise multiplication. Modern deep learning frameworks like TensorFlow and PyTorch provide built-in LSTM layers that handle these computations efficiently through optimized GPU operations.