Deep Learning / sequence-models

RNN and sequence models

Hidden state, BPTT, teacher forcing, LSTM/GRU gates, encoder-decoder models, and attention over states.

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Focus
  • sequence modeling
  • vanishing gradients
  • LSTM and GRU
  • attention over encoder states
Future feature idea

Sequence model timeline for hidden state, BPTT, LSTM/GRU gates, and attention over states.

Self-check
  1. What is the core job of "RNN and sequence models"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.