Lstm

Lab 2.3: Gates as Memory Engineering — LSTM from scratch, on a task that was supposed to kill the RNN Language-model internals series. Lab 2.2 measured the diagnosis instead of reciting it: on a trained char-RNN, the forward echo of a character and the backward BPTT blame both halve every ~2.3 steps (per-step retention 0.74), because the transported term per step is (roughly) $W_{hh}^{\top}\mathrm{diag}(1-h^2)$ — every step multiplies your memory by something whose average magnitude is far below 1. ...