Julian Schwinger, winner of the 1965 Nobel Prize in Physics. Original caption: "His laboratory is his ballpoint pen." Julian Seymour Schwinger (/ˈʃwɪŋər/; February 12, 1918 – July 16, 1994) was a Nobel Prize-winning American theoretical physicist. He is best known for his work on quantum electrodynamics (QED), in particular for developing a relativistically invariant perturbation theory, and for r
You can help expand this article with text translated from the corresponding article in Italian. (May 2023) Click [show] for important translation instructions. View a machine-translated version of the Italian article. Machine translation, like DeepL or Google Translate, is a useful starting point for translations, but translators must revise errors as necessary and confirm that the translation is
Sir Demis Hassabis CBE FRS FREng FRSA[2][3] (born 27 July 1976) is a British computer scientist, artificial intelligence researcher and entrepreneur. In his early career he was a video game AI programmer and designer, and an expert board games player.[4][5][6] He is the chief executive officer and co-founder of DeepMind[7] and Isomorphic Labs,[8][9][10] and a UK Government AI Advisor.[11] He is a
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note[1] with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote. This name reflects the fac
Stochastic approximation methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive update rules of stochastic approximation methods can be used, among other things, for solving linear systems when the collected data is corrupted by noise, or for approximating extreme values of functions which cannot be computed directly, but only
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated.[1] It has been used in many fields including econometrics, chemistry, and engineering.[2] Also known as Tikhonov regularization, named for Andrey Tikhonov, it is a method of regularization of ill-posed problems.[a] It is particularly useful t
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to t
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