Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium: Frontmatter

By Carolo Friederico Gauss. 2011

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Deep learning in neural networks: An overview (2014, methodology): “ld, 1982). In a sense NNs have been around even longer, since early supervised NNs were essentially variants of linear regression methods going back at least to the early 1800s (e.g., Legendre, 1805; Gauss, 1809, 1821); Gauss also refers to his work of 1795. Early NNs had a maximal CAP depth of 1 (S”
Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning (2015, background): “Informally, the linear regression problem is the task of determining β ∗ from the data ( X 1 , Y 1 ) , ( X 2 , Y 2 ) , . . . .”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “Linear regression models were used by Gauss, Legendre, and Quetelet [109,64,37,90] as early as the beginning of the 19th century and have since then grown into a vast array of regression analysis tools [115,98], for example, generalized additive models [45] and elastic net [132].”