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Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weightsandstructure determination (WASD) algorithm. Based on the authors 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet, and allows reader to extend the techniques in the book to solve scientific and engineering problems. The book will be of interest to engineers, senior undergraduates, postgraduates, and researchers in the fields of neuronets, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, simulation and modeling, deep learning, and data mining.FeaturesFocuses on neuronet models, algorithms, and applicationsDesigns, constructs, develops, analyzes, simulates and compares various WASD neuronet models, such as singleinput WASD neuronet models, twoinput WASD neuronet models, threeinput WASD neuronet models, and general multiinput WASD neuronet models for function data approximationsIncludes realworld applications, such as population predictionProvides complete mathematical foundations, such as Weierstrass approximation, Bernstein polynomial approximation, Taylor polynomial approximation, and multivariate function approximation, exploring the close integration of mathematics (i.e., function approximation theories) and computers (e.g., computer algorithms)Utilizes the authors' 20 years of research on neuronets
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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