concept:padding-masks
Padding Masks
Padding Masks in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Sequence ML: RNNs, GRUs & LSTMs
Sequences are not IID
In sequence learning, chronology is part of the feature schema: define the cutoff first, then construct inputs, labels, and splits around it.
Sequence ML: RNNs, GRUs & LSTMsRecurrent state and BPTT
Recurrent state carries information forward; BPTT carries responsibility backward, and truncation decides how far that responsibility can travel.
Sequence ML: RNNs, GRUs & LSTMsGates that remember: LSTM and GRU
Gates do not create memory by magic; they create learnable paths for preserving, replacing, and exposing state.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.