List the key components of LSTM

by
  • Gates (forget, Memory, update, and Read)
  • Tanh(x) (values between −1 and 1)
  • Sigmoid(x) (values between 0 and 1)

In an interview setting, providing a comprehensive answer to the question about the key components of Long Short-Term Memory (LSTM) networks would demonstrate your understanding of this fundamental architecture in artificial intelligence and recurrent neural networks. Here’s a breakdown of the key components:

  1. Cell State (c_t): The primary component that carries information across timesteps. It’s analogous to a conveyor belt, allowing information to flow through unchanged, enabling LSTMs to maintain long-term dependencies.
  2. Forget Gate (f_t): Determines what information to discard or forget from the cell state. It takes input from the previous timestep’s hidden state (h_{t-1}) and the current input (x_t), producing a value between 0 and 1 for each element in the cell state. 1 means “completely keep this” while 0 means “completely forget this.”
  3. Input Gate (i_t): Decides which new information to incorporate into the cell state. Similar to the forget gate, it takes input from h_{t-1} and x_t, but it produces a candidate value (g_t) to be added to the cell state.
  4. Candidate State (g_t): A new candidate value that could be added to the cell state, calculated by the input gate. It is a vector of new candidate values that might be added to the state.
  5. Output Gate (o_t): Determines what information from the cell state to output as the hidden state. It’s computed similarly to the forget and input gates, using h_{t-1} and x_t, but it’s based on the updated cell state.
  6. Hidden State (h_t): The output of the LSTM unit for a particular timestep, which encapsulates information about the sequence up to that point. It’s produced using the output gate and the modified cell state.
  7. Activation Functions: Typically, LSTM units use the sigmoid function for the gates (forget gate, input gate, output gate) to squish values between 0 and 1, and the hyperbolic tangent function (tanh) for the candidate state. These activation functions help regulate the flow of information and control the output range.
  8. Weight Matrices and Bias Vectors: Parameters of the LSTM network that are learned during training. These matrices and vectors are used in computing the gates, candidate state, and output, allowing the network to adapt and learn from the input data.

Understanding these components and how they interact is crucial for effectively implementing and utilizing LSTM networks in various applications, such as natural language processing, time series prediction, and sequence generation.