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Foundations and Advances in Deep Learning

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Figure

Figure 2.1. Illustrations of linear regression and perceptron networks. Note that the outputs of these two networks use different activation functions.
Figure 2.3. Illustrations of a linear autoencoder and Hopfield network. An undirected edge in the Hop- Hop-field network indicates that signal flows in both ways.
Figure 2.4. Illustrations of the naive Bayes classifier and probabilistic principal component analysis.
Figure 3.2. Illustrations of (a) the maximum-margin principle and (b) constructing a multilayer percep- percep-tron equivalent to a given support vector machine
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