LSTM: Why It Was Born, How It Fixes RNN, and Why It Changed Sequence Learning

It all ties together beautifully, showing how the LSTM architecture addresses key challenges in sequence modeling and long-term dependency handling. Here's a concise summary of the main points:

1. Cell State (Cₜ):

   • Acts as an internal memory that accumulates information over time.
   • Remains unbounded but is designed to preserve long-term context.

2. Tanh Function:

   • Normalizes and compresses the cell state, ensuring it stays within a manageable range (-1 to 1).
   • Prevents exploding activations and ensures stability during training.

3. Output Gate (oₜ):

   • Decides how much of the normalized cell state should be exposed as the hidden state.
   • Scales from 0 to 1, controlling the amount but not the magnitude.

4. Hidden State (hₜ):

   • Computed as ( h_t = o_t \odot \tanh(C_t) ).
   • Combines normalization and filtering for stable gradient flow and output control.

5. Gradient Flow:

   • LSTM separates gradient flow into two paths: a nonlinear exposed path through the hidden state (hₜ) and a stable memory

Read the full article at Towards AI - Medium

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