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doc/pub/week42/html/week42.html

@@ -215,6 +215,7 @@ div { text-align: justify; text-justify: inter-word; }
                'layers-used-to-build-cnns'),
               ('Transforming images', 2, None, 'transforming-images'),
               ('CNNs in brief', 2, None, 'cnns-in-brief'),
+              ('Key Idea', 2, None, 'key-idea'),
               ('Mathematics of CNNs', 2, None, 'mathematics-of-cnns'),
               ('Convolution Examples: Polynomial multiplication',
                2,
@@ -3235,6 +3236,24 @@ the course
 <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" target="_blank">IN5400 &#8211; Machine Learning for Image Analysis</a>
 and the slides of <a href="http://cs231n.github.io/convolutional-networks/" target="_blank">CS231</a> which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). <a href="http://neuralnetworksanddeeplearning.com/chap6.html" target="_blank">Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs</a>.
 
+<p>
+The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="key-idea">Key Idea </h2>
+
+<p>
+A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.
+
+<p>
+The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect
+only neighboring neurons in the input instead of connecting all with the first hidden layer.
+
+<p>
+We say we perform a filtering (convolution is the mathematical operation).
+
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>