This project is focused on Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells. RNNs are a type of neural network that can process sequential data, such as time series or natural language. LSTM is a type of RNN that can handle long-term dependencies and avoid the vanishing gradient problem.
RNNs process sequential data by maintaining a hidden state that is updated at each time step. The hidden state is a vector that summarizes the information from the previous time steps. LSTM cells are a type of RNN that have a more complex structure than simple RNNs. They have a cell state that can store information over long periods of time, and three gates that control the flow of information into and out of the cell state.
The three gates are:
- Forget gate: decides which information to discard from the cell state
- Input gate: decides which new information to add to the cell state
- Output gate: decides which information to output from the cell state
LSTM cells can learn to selectively remember or forget information based on the input data, which makes them well-suited for tasks such as language modeling, speech recognition, and machine translation.
This diagram shows the unrolled structure of an LSTM cell in an RNN. The input at each time step is fed into the LSTM cell, which updates its hidden state and cell state. The hidden state is then passed to the next time step, and so on. The three gates (forget, input, and output) control the flow of information into and out of the cell state.
To get started with this project, clone the repository and install the required dependencies. You can then train and evaluate an LSTM model on your own dataset.