diff options
| author | Yuchen Pei <me@ypei.me> | 2018-05-11 15:48:59 +0200 |
|---|---|---|
| committer | Yuchen Pei <me@ypei.me> | 2018-05-11 15:48:59 +0200 |
| commit | a24d26ee19831f3fb10b7d78d76a7e0dcc5a510b (patch) | |
| tree | 20652d597c4aeb6da42f8cbfb7669710834a24cb /microposts | |
| parent | 67a012fdfbd62c20b25ef86da387c436bc659649 (diff) | |
minor edit
- added link to permalink of a previous micropost
Diffstat (limited to 'microposts')
| -rw-r--r-- | microposts/rnn-fsm.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/microposts/rnn-fsm.md b/microposts/rnn-fsm.md index 567063d..a1e1315 100644 --- a/microposts/rnn-fsm.md +++ b/microposts/rnn-fsm.md @@ -3,7 +3,7 @@ date: 2018-05-11 --- ### Some notes on RNN, FSM / FA, TM and UTM -Related to a previous micropost. +Related to [a previous micropost](#neural-turing-machine). [The slides from Toronto](http://www.cs.toronto.edu/~rgrosse/csc321/lec9.pdf) is a nice introduction to RNN (recurrent neural network) from a computational point of view. It states that RNN can simulate any FSM (finite state machine, a.k.a. finite automata abbr. FA) with a toy example computing the parity of a binary string. |