How Long Short-Term Memory Algorithm will work?

 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network that use memory cells and gates to selectively store, retrieve and forget information over time.

The basic idea behind an LSTM is to allow the network to learn which information is important to keep in memory, and which information can be discarded.

An LSTM network is composed of multiple LSTM cells, which are connected to each other and to the input and output of the network. Each LSTM cell has three gates: the input gate, the forget gate, and the output gate.

1. Input gate: This gate controls which information from the input is passed to the memory cell. The input gate takes the input vector and the previous hidden state as inputs, and computes a vector of values between 0 and 1 that represent the importance of the input.

2. Forget gate: This gate controls which information from the memory cell should be discarded. The forget gate takes the input vector and the previous hidden state as inputs, and computes a vector of values between 0 and 1 that represent the importance of each element of the memory cell.

3. Output gate: This gate controls the output of the memory cell. The output gate takes the input vector and the previous hidden state as inputs, and computes a vector of values between 0 and 1 that represent the importance of each element of the memory cell for the current output.

The memory cell itself consists of a vector of values that can be read from or written to. The LSTM network can selectively read from or write to this memory cell, based on the input, forget, and output gates.

Overall, the LSTM algorithm works by allowing the network to selectively remember or forget information over time, based on the relevance of the information to the current task. This ability to store and retrieve information over long periods of time makes LSTMs particularly effective in tasks that involve sequential data, such as speech recognition and machine translation.