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Learning Deep Architectures for AI - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

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Figure

Fig. 1.1 We would like the raw input image to be transformed into gradually higher levels of representation, representing more and more abstract functions of the raw input, e.g., edges, local shapes, object parts, etc
Fig. 2.1 Examples of functions represented by a graph of computations, where each node is taken in some “element set” of allowed computations
Fig. 2.2 Example of polynomial circuit (with products on odd layers and sums on even ones) illustrating the factorization enjoyed by a deep architecture
Fig. 3.1 The set of images associated with the same object class forms a manifold or a set of disjoint manifolds, i.e., regions of lower dimension than the original space of images
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