The main difference between LSTM and Bi-LSTM is the direction in which they process input sequences. LSTM processes the input sequence in one direction (forward or backward), while Bi-LSTM processes the input sequence in two directions (both forward and backward).
LSTM uses a hidden state, memory cells, and gates (input, forget, and output gates) to maintain information for longer periods of time and handle the problem of vanishing gradients in traditional RNNs.
Bi-LSTM, on the other hand, uses two separate LSTMs to process the input sequence in forward and backward directions, respectively. The outputs of both LSTMs are then concatenated to produce a richer representation of the input sequence. This can lead to improved performance on certain tasks, such as sentiment analysis and named entity recognition, as it considers both past and future contextual information.
In short, LSTM processes the input sequence in one direction, while Bi-LSTM processes the input sequence in two directions to capture both past and future contextual information.
