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The individual elements of the network, the neurons, are simple. Feature extraction functions behave like tinted glasses, highlighting some of the properties colors in our data and making it impossible to see other properties.



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Weight normalization is a reparameterization that decouples the magnitude of a weight tensor from its direction. In naive Bayes classifiers, every feature gets a say in determining which label should be assigned to a given input value.

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If we could instead look at the effect of these features across the entire training set, then we might be able to make some conclusions about how they should affect the choice of label. Can be a single number or a one-element tuple sW,. However, in the next section, we'll look at a classifier that considers the possible interactions between these parameters when choosing their values.


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  1. Think of a neural network monitoring your daily routine over a long period of time. We need to check if the sender has arrived at location b, and if it has, feed forward that output to the next neuron.

  2. For each element in the input sequence, each layer computes the following function: