deep-learningsequencesrnn
LSTM and GRU
Gated recurrent units that solved vanishing gradients — the workhorse of sequence modelling before transformers.
Уровни глубины
L0Intro~2ч
Knows LSTM has "memory cells" that persist across time.
L1Basics~10ч
Draws LSTM gates (input, forget, output); implements a language model.
L2Working~15ч
Compares LSTM vs GRU vs BiLSTM; uses teacher forcing; gradient clipping for stability.
L3Advanced~20ч
Analyses peephole connections, layered/residual RNNs; attention on top of LSTM.
L4Research~40ч
Linear-time alternatives (Mamba, SSMs) and their connection to gated RNNs.