What is LSTM, and how does it work?

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A Long Short-Term Memory (LSTM) network is an advanced type of Recurrent Neural Network (RNN) designed to overcome the limitations of traditional RNNs, especially the vanishing and exploding gradient problems. These problems make it difficult for standard RNNs to learn long-term dependencies in sequential data. LSTMs solve this using a special architecture with a cell state and gates that control the flow of information.

How LSTM Works

An LSTM unit has three main gates and a cell state:

1. Cell State ($C_t$)

   • Acts as the “memory” of the network, carrying important information across many time steps.

2. Forget Gate

   • Decides what information to discard from the cell state.

   • Formula:

     $$f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)$$

3. Input Gate

   • Decides what new information to add to the cell state.

   • Formula:

     $$i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i)$$

   • Candidate values:

     $$\tilde{C}_t = \tanh(W_C \cdot [h_{t-1}, x_t] + b_C)$$

4. Update Cell State

   • Combines forget and input gate results:

     $$C_t = f_t \cdot C_{t-1} + i_t \cdot \tilde{C}_t$$

5. Output Gate

   • Decides the next hidden state ($h_t$) and output.

   • Formula:

     $$o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o)$$ $$h_t = o_t \cdot \tanh(C_t)$$

Key Features of LSTM

• Maintains long-term dependencies using its cell state.

• Avoids vanishing gradient by allowing constant error flow through gates.

• Learns what to remember and what to forget automatically.

Applications

• Natural Language Processing (NLP): language translation, text generation, sentiment analysis

• Speech Recognition

• Time-Series Forecasting: stock prediction, weather forecasting

• Anomaly Detection in sequential data

👉 In short, LSTMs are RNNs with memory control mechanisms (gates) that make them highly effective for learning long-term patterns in sequential data.

🔑Read More:

Explain CNN (Convolutional Neural Network).

Explain RNN (Recurrent Neural Network).

What is gradient descent?

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