-
diff --git a/doc/pub/cnn/html/._cnn-bs001.html b/doc/pub/cnn/html/._cnn-bs001.html index b8822668b..beaef9c0f 100644 --- a/doc/pub/cnn/html/._cnn-bs001.html +++ b/doc/pub/cnn/html/._cnn-bs001.html @@ -151,9 +151,15 @@ MathJax.Hub.Config({
-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 diff --git a/doc/pub/cnn/html/cnn-bs.html b/doc/pub/cnn/html/cnn-bs.html index b24bc014c..204a8fa89 100644 --- a/doc/pub/cnn/html/cnn-bs.html +++ b/doc/pub/cnn/html/cnn-bs.html @@ -167,7 +167,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/cnn/html/cnn-reveal.html b/doc/pub/cnn/html/cnn-reveal.html index e18534af6..238e1188a 100644 --- a/doc/pub/cnn/html/cnn-reveal.html +++ b/doc/pub/cnn/html/cnn-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -162,9 +162,15 @@ MathJax.Hub.Config({
-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 diff --git a/doc/pub/cnn/html/cnn-solarized.html b/doc/pub/cnn/html/cnn-solarized.html index 405147bdf..48df0b9ef 100644 --- a/doc/pub/cnn/html/cnn-solarized.html +++ b/doc/pub/cnn/html/cnn-solarized.html @@ -110,7 +110,7 @@ MathJax.Hub.Config({
-
@@ -118,9 +118,15 @@ MathJax.Hub.Config({
-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 diff --git a/doc/pub/cnn/html/cnn.html b/doc/pub/cnn/html/cnn.html index d9b2a68bc..edfec6f96 100644 --- a/doc/pub/cnn/html/cnn.html +++ b/doc/pub/cnn/html/cnn.html @@ -115,7 +115,7 @@ MathJax.Hub.Config({
-
@@ -123,9 +123,15 @@ MathJax.Hub.Config({
-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 diff --git a/doc/pub/cnn/ipynb/cnn.ipynb b/doc/pub/cnn/ipynb/cnn.ipynb index c8b65b602..e36994940 100644 --- a/doc/pub/cnn/ipynb/cnn.ipynb +++ b/doc/pub/cnn/ipynb/cnn.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 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", diff --git a/doc/pub/cnn/ipynb/ipynb-cnn-src.tar.gz b/doc/pub/cnn/ipynb/ipynb-cnn-src.tar.gz index 83c1ebe27..145268b2f 100644 Binary files a/doc/pub/cnn/ipynb/ipynb-cnn-src.tar.gz and b/doc/pub/cnn/ipynb/ipynb-cnn-src.tar.gz differ diff --git a/doc/pub/cnn/pdf/cnn-minted.pdf b/doc/pub/cnn/pdf/cnn-minted.pdf index 874429fa7..4f60d0386 100644 Binary files a/doc/pub/cnn/pdf/cnn-minted.pdf and b/doc/pub/cnn/pdf/cnn-minted.pdf differ diff --git a/doc/src/CNN/cnn.do.txt b/doc/src/CNN/cnn.do.txt index 6ead64ef5..40b715e51 100644 --- a/doc/src/CNN/cnn.do.txt +++ b/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