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A one-step gradient descent based weight update known as Langevin-gradient (LG) proposal distribution ( Eq 4), Deyle ER, Sugihara G. Generalized theorems for nonlinear state space reconstruction. Plos one. 2011;6(3):e18295. pmid:21483839 Senator Ben Murray-Bruce represented Bayelsa East Senatorial District at the National Assembly. From Akassa in Bayelsa State of Nigeria. [72]

Ikein, Augustine (2004). "Economic Development Agenda for Bayelsa State of Nigeria: An Advisor's Opinion" (PDF). Journal of Sustainable Development in Africa. Archived (PDF) from the original on 2009-01-07. Schmidhuber J. Deep learning in neural networks: An overview. Neural networks. 2015;61:85–117. pmid:25462637Shiskin, J. and T. J. Plewes, (1978) Seasonal Adjustment of the U.S. Unemployment Rate The Statistician 27, 181–202.

Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. Journal of Big Data. 2015;2(1):1–21.Bayelsa Most Affected By Climate Change Due to Environmental Degradation, Says Diri - THISDAYLIVE". www.thisdaylive.com . Retrieved 2023-06-30. Stochastic gradient descent (SGD) is one of the prominent methods of training neural networks. SGD is an iterative method to optimize a differentiable objective function with help of gradients [ 83]. In some high-dimensional optimization problems, SGD reduces the computational burden by achieving faster iterations with a lower convergence rate [ 83]. Training neural networks also can be considered as solving the non-convex optimization problem: