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Efficient deep neural network inference for embedded systems:A mixture of experts approach

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Fig. 2.5 A 2-cluster, 2-feature example of the K-means Clustering algorithm. (a) The initial random assignment of each data point, (b) Calculate the centroid of each cluster, (c) Re-assign data points to the nearest centroid, (d) Re-calculate centroids of
Fig. 2.6 An example of a simple decision tree. F1, F2, etc. represent different features, and each box is a different decision in the tree
Fig. 2.9 A simple neural network consisting of 3 layers. Blue circles represent individual neurons, which are grouped into layers that are represented by the orange rectangles.
Fig. 2.14 How the data value changes shape as it passes through each layer. Equation 2.1 shows the equation used to calculate output sizes
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