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CNN.org

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+* Features
+
+CNNs compare images piece by piece. The pieces that it looks for are called
+features. By finding rough feature matches in roughly the same positions in two
+images, CNNs get a lot better at seeing similarity than whole-image matching
+schemes.
+
+* Convolution
+
++ CNN will try to match the feature everywhere and in every possible position.
++ In calculating the match to a feature across the whole image, we call it a *filter*
++ to calculate the match of a feature to a patch of the image, simply multiply
+  each pixel in the feature by the value of the corresponding pixel in the image
++ then add up the values and divide by the total number of pixels
++ to complete convolution, repeat the process, lining up the feature with every
+  possible image patch
++ values close to 1 show strong matches, close to -1 indicate photographic
+  negative of our feature, 0 means no match
+
+* Pooling
+
+- way to take large images and shrink them down while preserving the most
+  important information
+- stepping a small window across an image and taking the maximum value from the
+  window as each step.
+
+* Fully Connected Layers
+
+- take high level filtered images and translates them into votes
+- we have decide between X categories
+- some values are better than others in classifying a particular class, these
+  get larger votes than others
+- these votes are expressed as weights or connection strengths