Toward Deep Neural Networks: WASD Neuronet Models Algorithms and Applications introduces the outlook and extension toward deep neural networks with a focus on the weights-and-structure 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 single-input WASD neuronet models two-input WASD neuronet models three-input WASD neuronet models and general multi-input WASD neuronet models for function data approximationsIncludes real-world 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 | Toward Deep Neural Networks WASD Neuronet Models Algorithms and Applications