Abstract
This chapter introduces the topic of optimization. It outlines the key ideas in an intuitive fashion. Furthermore, the distinction between supervised and unsupervised learning is explained, and their strengths and weaknesses are discussed. For both learning approaches, the choice of the loss function and their implications are discussed in detail.
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Notes
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Some “exotic” distributions (e.g., the Cauchy distribution) exist that do not meet the so-called Lindeberg condition, and therefore their sum is not asymptotically Gaussian distributed. But these exceptions are without practical significance in the context of this book.
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Plate, T.: Re: Kangaroos (Was Re: BackProp without Calculus?). Usenet article <93Sep8.162519edt.997@neuron.ai.toronto.edu> in comp.ai.neural-nets (1993)
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Nelles, O. (2020). Introduction to Optimization. In: Nonlinear System Identification. Springer, Cham. https://doi.org/10.1007/978-3-030-47439-3_2
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DOI: https://doi.org/10.1007/978-3-030-47439-3_2
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