L19/5 Truncated Backprop through Time

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Dive into Deep Learning
UC Berkeley, STAT 157

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The book is at

Truncated Backprop
  1. Alex Smola
  2. D2L
  3. Dive into Deep Learning
  4. STAT-157
  5. Apache MXNet
  6. Gluon
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In slide for 'Latent state gradient'(video time 10:44), why does he use different index for `x` and `h` in function `f` which is used for hidden state ?(j for x, j-1 for h) In previous slide, he used same index(t-1), h_t = f(h_t-1, x_t-1, w). What am I missing here?

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