-
diff --git a/doc/pub/Recurrent/html/._Recurrent-bs001.html b/doc/pub/Recurrent/html/._Recurrent-bs001.html index 050a9a1bb..2520956c0 100644 --- a/doc/pub/Recurrent/html/._Recurrent-bs001.html +++ b/doc/pub/Recurrent/html/._Recurrent-bs001.html @@ -111,10 +111,9 @@ MathJax.Hub.Config({
-Till now our focus has been, including convolutional neural networks as well, -on feedforward neural networks. The output or the -activations flow only in one direction, from the input layer to the -output layer. +Till now our focus has been, including convolutional neural networks +as well, on feedforward neural networks. The output or the activations +flow only in one direction, from the input layer to the output layer.
A recurrent neural network (RNN) looks very much like a feedforward @@ -132,7 +131,7 @@ input, making them extremely useful for natural language processing systems such as automatic translation and speech-to-text.
-The text here is under development. Planned finished Jan 2020. +The text here is under development. Planned finished mid Jan 2020.
diff --git a/doc/pub/Recurrent/html/._Recurrent-bs002.html b/doc/pub/Recurrent/html/._Recurrent-bs002.html index 44852713c..6e1c74831 100644 --- a/doc/pub/Recurrent/html/._Recurrent-bs002.html +++ b/doc/pub/Recurrent/html/._Recurrent-bs002.html @@ -118,11 +118,10 @@ to the inputs \( x_t \), the layer at a time \( t \) receives also as input 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. +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.
diff --git a/doc/pub/Recurrent/html/Recurrent-bs.html b/doc/pub/Recurrent/html/Recurrent-bs.html index faf97064a..e783097bf 100644 --- a/doc/pub/Recurrent/html/Recurrent-bs.html +++ b/doc/pub/Recurrent/html/Recurrent-bs.html @@ -127,7 +127,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/Recurrent/html/Recurrent-reveal.html b/doc/pub/Recurrent/html/Recurrent-reveal.html index 864eec463..4dbde02bd 100644 --- a/doc/pub/Recurrent/html/Recurrent-reveal.html +++ b/doc/pub/Recurrent/html/Recurrent-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -162,10 +162,9 @@ MathJax.Hub.Config({
-Till now our focus has been, including convolutional neural networks as well, -on feedforward neural networks. The output or the -activations flow only in one direction, from the input layer to the -output layer. +Till now our focus has been, including convolutional neural networks +as well, on feedforward neural networks. The output or the activations +flow only in one direction, from the input layer to the output layer.
A recurrent neural network (RNN) looks very much like a feedforward @@ -183,7 +182,7 @@ input, making them extremely useful for natural language processing systems such as automatic translation and speech-to-text.
-The text here is under development. Planned finished Jan 2020. +The text here is under development. Planned finished mid Jan 2020. @@ -198,11 +197,10 @@ to the inputs \( x_t \), the layer at a time \( t \) receives also as input 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. +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. @@ -210,7 +208,15 @@ Add figures and bring up equations.
-Have example with eigenvalues solvers as well. +In our discussions of ordinary differential equations and partial +differential equations using neural networks. Here we will discuss how +we can solve say ordinary differential equations and eigenvalue +problems using RNNs. Eigenvalue problems can be solved using RNNs by +rewriting such a problems as a non-linear differential equation. + +
+Instead of starting with a well-known ordinary differential equation, +we start directly with an eigenvaule problem. diff --git a/doc/pub/Recurrent/html/Recurrent-solarized.html b/doc/pub/Recurrent/html/Recurrent-solarized.html index 4a1b3e983..69aa43204 100644 --- a/doc/pub/Recurrent/html/Recurrent-solarized.html +++ b/doc/pub/Recurrent/html/Recurrent-solarized.html @@ -87,7 +87,7 @@ MathJax.Hub.Config({
-
@@ -95,10 +95,9 @@ MathJax.Hub.Config({
-Till now our focus has been, including convolutional neural networks as well, -on feedforward neural networks. The output or the -activations flow only in one direction, from the input layer to the -output layer. +Till now our focus has been, including convolutional neural networks +as well, on feedforward neural networks. The output or the activations +flow only in one direction, from the input layer to the output layer.
A recurrent neural network (RNN) looks very much like a feedforward @@ -116,7 +115,7 @@ input, making them extremely useful for natural language processing systems such as automatic translation and speech-to-text.
-The text here is under development. Planned finished Jan 2020. +The text here is under development. Planned finished mid Jan 2020.
@@ -131,11 +130,10 @@ to the inputs \( x_t \), the layer at a time \( t \) receives also as input
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. +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.
@@ -143,7 +141,15 @@ Add figures and bring up equations.
-Have example with eigenvalues solvers as well. +In our discussions of ordinary differential equations and partial +differential equations using neural networks. Here we will discuss how +we can solve say ordinary differential equations and eigenvalue +problems using RNNs. Eigenvalue problems can be solved using RNNs by +rewriting such a problems as a non-linear differential equation. + +
+Instead of starting with a well-known ordinary differential equation, +we start directly with an eigenvaule problem.
diff --git a/doc/pub/Recurrent/html/Recurrent.html b/doc/pub/Recurrent/html/Recurrent.html
index 2f9b30def..659a84bf1 100644
--- a/doc/pub/Recurrent/html/Recurrent.html
+++ b/doc/pub/Recurrent/html/Recurrent.html
@@ -92,7 +92,7 @@ MathJax.Hub.Config({
-
@@ -100,10 +100,9 @@ MathJax.Hub.Config({
-Till now our focus has been, including convolutional neural networks as well, -on feedforward neural networks. The output or the -activations flow only in one direction, from the input layer to the -output layer. +Till now our focus has been, including convolutional neural networks +as well, on feedforward neural networks. The output or the activations +flow only in one direction, from the input layer to the output layer.
A recurrent neural network (RNN) looks very much like a feedforward @@ -121,7 +120,7 @@ input, making them extremely useful for natural language processing systems such as automatic translation and speech-to-text.
-The text here is under development. Planned finished Jan 2020. +The text here is under development. Planned finished mid Jan 2020.
@@ -136,11 +135,10 @@ to the inputs \( x_t \), the layer at a time \( t \) receives also as input
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. +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.
@@ -148,7 +146,15 @@ Add figures and bring up equations.
-Have example with eigenvalues solvers as well. +In our discussions of ordinary differential equations and partial +differential equations using neural networks. Here we will discuss how +we can solve say ordinary differential equations and eigenvalue +problems using RNNs. Eigenvalue problems can be solved using RNNs by +rewriting such a problems as a non-linear differential equation. + +
+Instead of starting with a well-known ordinary differential equation, +we start directly with an eigenvaule problem.
diff --git a/doc/pub/Recurrent/ipynb/Recurrent.ipynb b/doc/pub/Recurrent/ipynb/Recurrent.ipynb
index 8e583fc12..35d6e91b7 100644
--- a/doc/pub/Recurrent/ipynb/Recurrent.ipynb
+++ b/doc/pub/Recurrent/ipynb/Recurrent.ipynb
@@ -10,7 +10,7 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Dec 16, 2019**\n",
+ "Date: **Dec 17, 2019**\n",
"\n",
"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -19,10 +19,9 @@
"\n",
"## Recurrent neural networks: Overarching view\n",
"\n",
- "Till now our focus has been, including convolutional neural networks as well, \n",
- "on feedforward neural networks. The output or the\n",
- "activations flow only in one direction, from the input layer to the\n",
- "output layer.\n",
+ "Till now our focus has been, including convolutional neural networks\n",
+ "as well, on feedforward neural networks. The output or the activations\n",
+ "flow only in one direction, from the input layer to the output layer.\n",
"\n",
"A recurrent neural network (RNN) looks very much like a feedforward\n",
"neural network, except that it also has connections pointing\n",
@@ -37,7 +36,7 @@
"input, making them extremely useful for natural language processing\n",
"systems such as automatic translation and speech-to-text.\n",
"\n",
- "**The text here is under development**. Planned finished Jan 2020.\n",
+ "**The text here is under development**. Planned finished mid Jan 2020.\n",
"\n",
"\n",
"## Set up of an RNN\n",
@@ -48,17 +47,26 @@
"to the inputs $x_t$, the layer at a time $t$ receives also as input\n",
"the output from the previous layer $t-1$, that is $y_{t1}$.\n",
"\n",
- "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\n",
- "the output from the previous time $y_{t-1}$ and $y_t$. The figure here shows an example of a simple RNN.\n",
- "\n",
- "\n",
- "Add figures and bring up equations. \n",
+ "This means also that we need to have weights that link both the inputs\n",
+ "$x_t$ to the outputs $y_t$ as well as weights that link the output\n",
+ "from the previous time $y_{t-1}$ and $y_t$. The figure here shows an\n",
+ "example of a simple RNN.\n",
"\n",
"\n",
"\n",
"## Solving differential equations and eigenvalue problems with RNNs\n",
"\n",
- "Have example with eigenvalues solvers as well.\n",
+ "\n",
+ "\n",
+ "In our discussions of ordinary differential equations and partial\n",
+ "differential equations using neural networks. Here we will discuss how\n",
+ "we can solve say ordinary differential equations and eigenvalue\n",
+ "problems using RNNs. Eigenvalue problems can be solved using RNNs by\n",
+ "rewriting such a problems as a non-linear differential equation.\n",
+ "\n",
+ "Instead of starting with a well-known ordinary differential equation,\n",
+ "we start directly with an eigenvaule problem.\n",
+ "\n",
"\n",
"\n",
"## Long-Short Time Memory\n",
diff --git a/doc/pub/Recurrent/ipynb/ipynb-Recurrent-src.tar.gz b/doc/pub/Recurrent/ipynb/ipynb-Recurrent-src.tar.gz
index 7a09d6f0f..00aa09c14 100644
Binary files a/doc/pub/Recurrent/ipynb/ipynb-Recurrent-src.tar.gz and b/doc/pub/Recurrent/ipynb/ipynb-Recurrent-src.tar.gz differ
diff --git a/doc/pub/Recurrent/pdf/Recurrent-minted.pdf b/doc/pub/Recurrent/pdf/Recurrent-minted.pdf
index 6a7bf9437..c14161c68 100644
Binary files a/doc/pub/Recurrent/pdf/Recurrent-minted.pdf and b/doc/pub/Recurrent/pdf/Recurrent-minted.pdf differ
diff --git a/doc/src/Recurrent/Recurrent.do.txt b/doc/src/Recurrent/Recurrent.do.txt
index ae8d5c6b2..3e050035e 100644
--- a/doc/src/Recurrent/Recurrent.do.txt
+++ b/doc/src/Recurrent/Recurrent.do.txt
@@ -6,10 +6,9 @@ DATE: today
!split
===== Recurrent neural networks: Overarching view =====
-Till now our focus has been, including convolutional neural networks as well,
-on feedforward neural networks. The output or the
-activations flow only in one direction, from the input layer to the
-output layer.
+Till now our focus has been, including convolutional neural networks
+as well, on feedforward neural networks. The output or the activations
+flow only in one direction, from the input layer to the output layer.
A recurrent neural network (RNN) looks very much like a feedforward
neural network, except that it also has connections pointing
@@ -24,7 +23,7 @@ example, they can take sentences, documents, or audio samples as
input, making them extremely useful for natural language processing
systems such as automatic translation and speech-to-text.
-_The text here is under development_. Planned finished Jan 2020.
+_The text here is under development_. Planned finished mid Jan 2020.
!split
@@ -36,18 +35,27 @@ offers an intutitive way of understanding these networks. In addition
to the inputs $x_t$, the layer at a time $t$ receives also as input
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.
+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.
!split
===== Solving differential equations and eigenvalue problems with RNNs =====
-Have example with eigenvalues solvers as well.
+
+
+In our discussions of ordinary differential equations and partial
+differential equations using neural networks. Here we will discuss how
+we can solve say ordinary differential equations and eigenvalue
+problems using RNNs. Eigenvalue problems can be solved using RNNs by
+rewriting such a problems as a non-linear differential equation.
+
+Instead of starting with a well-known ordinary differential equation,
+we start directly with an eigenvaule problem.
+
!split