adding some more motivation to cnn

doc/pub/cnn/html/._cnn-bs000.html

@@ -167,7 +167,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 16, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/cnn/html/._cnn-bs001.html

@@ -151,9 +151,15 @@ MathJax.Hub.Config({
 <h2 id="___sec0" class="anchor">Convolutional Neural Networks (recognizing images) </h2>
 
 <p>
-Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
+Convolutional neural networks (CNNs) were developed during the last
+decade of the previous century, with a focus on character recognition
+tasks. Nowadays, CNNs are a central element in the spectacular success
+of dee learning methods. The success in for example image
+classifications have made them a central tool for most machine
+learning practitioners.
 
 <p>
+CNNs are very similar to ordinary Neural Networks.
 They are made up of neurons that have learnable weights and
 biases. Each neuron receives some inputs, performs a dot product and
 optionally follows it with a non-linearity. The whole network still

doc/pub/cnn/html/cnn-bs.html

@@ -167,7 +167,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 16, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/cnn/html/cnn-reveal.html

@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Dec 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 16, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -162,9 +162,15 @@ MathJax.Hub.Config({
 <h2 id="___sec0">Convolutional Neural Networks (recognizing images) </h2>
 
 <p>
-Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
+Convolutional neural networks (CNNs) were developed during the last
+decade of the previous century, with a focus on character recognition
+tasks. Nowadays, CNNs are a central element in the spectacular success
+of dee learning methods. The success in for example image
+classifications have made them a central tool for most machine
+learning practitioners.
 
 <p>
+CNNs are very similar to ordinary Neural Networks.
 They are made up of neurons that have learnable weights and
 biases. Each neuron receives some inputs, performs a dot product and
 optionally follows it with a non-linearity. The whole network still

doc/pub/cnn/html/cnn-solarized.html

@@ -110,7 +110,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 16, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -118,9 +118,15 @@ MathJax.Hub.Config({
 <h2 id="___sec0">Convolutional Neural Networks (recognizing images) </h2>
 
 <p>
-Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
+Convolutional neural networks (CNNs) were developed during the last
+decade of the previous century, with a focus on character recognition
+tasks. Nowadays, CNNs are a central element in the spectacular success
+of dee learning methods. The success in for example image
+classifications have made them a central tool for most machine
+learning practitioners.
 
 <p>
+CNNs are very similar to ordinary Neural Networks.
 They are made up of neurons that have learnable weights and
 biases. Each neuron receives some inputs, performs a dot product and
 optionally follows it with a non-linearity. The whole network still

doc/pub/cnn/html/cnn.html

@@ -115,7 +115,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 16, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -123,9 +123,15 @@ MathJax.Hub.Config({
 <h2 id="___sec0">Convolutional Neural Networks (recognizing images) </h2>
 
 <p>
-Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
+Convolutional neural networks (CNNs) were developed during the last
+decade of the previous century, with a focus on character recognition
+tasks. Nowadays, CNNs are a central element in the spectacular success
+of dee learning methods. The success in for example image
+classifications have made them a central tool for most machine
+learning practitioners.
 
 <p>
+CNNs are very similar to ordinary Neural Networks.
 They are made up of neurons that have learnable weights and
 biases. Each neuron receives some inputs, performs a dot product and
 optionally follows it with a non-linearity. The whole network still

doc/pub/cnn/ipynb/cnn.ipynb

@@ -10,7 +10,7 @@
     "<!-- Author: -->  \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 14, 2019**\n",
+    "Date: **Dec 16, 2019**\n",
     "\n",
     "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -20,8 +20,15 @@
     "\n",
     "## Convolutional Neural Networks (recognizing images)\n",
     "\n",
-    "Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.\n",
     "\n",
+    "Convolutional neural networks (CNNs) were developed during the last\n",
+    "decade of the previous century, with a focus on character recognition\n",
+    "tasks. Nowadays, CNNs are a central element in the spectacular success\n",
+    "of dee learning methods. The success in for example image\n",
+    "classifications have made them a central tool for most machine\n",
+    "learning practitioners.\n",
+    "\n",
+    "CNNs are very similar to ordinary Neural Networks.\n",
     "They are made up of neurons that have learnable weights and\n",
     "biases. Each neuron receives some inputs, performs a dot product and\n",
     "optionally follows it with a non-linearity. The whole network still\n",

doc/src/CNN/cnn.do.txt

@@ -7,8 +7,15 @@ DATE: today
 !split
 ===== Convolutional Neural Networks (recognizing images) =====
 
-Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
 
+Convolutional neural networks (CNNs) were developed during the last
+decade of the previous century, with a focus on character recognition
+tasks. Nowadays, CNNs are a central element in the spectacular success
+of dee learning methods. The success in for example image
+classifications have made them a central tool for most machine
+learning practitioners.
+
+CNNs are very similar to ordinary Neural Networks.
 They are made up of neurons that have learnable weights and
 biases. Each neuron receives some inputs, performs a dot product and
 optionally follows it with a non-linearity. The whole network still