LSTM stands for Long Short-Term Memory, which is a type of recurrent neural network (RNN) architecture that is designed to process sequential data, such as speech, text, and time series data. Problem With RNNs In traditional RNNs, the hidden state of the network is updated based on the current input and the previous hidden state, which creates a feedback loop that allows the network to process sequential data. However, this feedback loop can cause the gradients to vanish or explode as the network processes longer sequences, which makes it difficult to learn long-term dependencies. Why LSTM invented LSTM networks address this problem by using memory cells that can selectively store and output information over time. The memory cells are controlled by gates that regulate the flow of information in and out of the cell. This allows the network to selectively remember or forget information from previous time steps, which enables it to effectively handle long-term dependencies. Let...