rnn disposition

2019-10-07 23:01:48 +02:00
parent 53d87cca90
commit 0738b0c283
2 changed files with 8 additions and 1 deletions
@@ -38,4 +38,8 @@ This means also that we need to have weights that link both the inputs $x_t$ to
 the output from the previous time $y_{t-1}$ and $y_t$.   The figure here shows an example of a simple RNN.


-Add figures and bring up equations. Have example with eigenvalues solvers
+Add figures and bring up equations. Have example with eigenvalues solvers as well.
+Discussions about dynamic unrolling through time. discuss memory cells, input and output
+
+Add about LSTM cell.
+Should add autoencoders? 
@@ -36,3 +36,6 @@ the output from the previous layer $t-1$, that is $y_{t1}$.

 This means also that we need to have weights that link both the inputs $x_t$ to the outputs $y_t$ as well as weights that link
 the output from the previous time $y_{t-1}$ and $y_t$.   The figure here shows an example of a simple RNN.
+
+
+Add figures and bring up equations. Have example with eigenvalues solvers