Titlebook: Geometry of Deep Learning; A Signal Processing Jong Chul Ye Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

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Mathematical PreliminariesIn this chapter, we briefly review the basic mathematical concepts that are required to understand the materials of this book.

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Reproducing Kernel Hilbert Space, Representer TheoremOne of the key concepts in machine learning is the feature space, which is often referred to as the .. A feature space is usually a higher or lower-dimensional space than the original one where the input data lie (which is often referred to as the .).

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Normalization and AttentionIn this chapter, we will discuss very exciting and rapidly evolving technical fields of deep learning: . and ..

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https://doi.org/10.1007/978-3-322-84383-8his method is mainly used to predict and find the cause-and-effect relationship between variables. For example, in a linear regression, a researcher tries to find the line that best fits the data according to a certain mathematical criterion (see Fig. 3.1a).

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https://doi.org/10.1007/978-3-642-91616-8f neurons and connections in a network may be significantly high. One of the amazing aspects of biological neural networks is that when the neurons are connected to each other, higher-level intelligence, which cannot be observed from a single neuron, emerges.

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