Название: Neural Networks and Numerical AnalysisАвтор: Bruno Despres
Издательство: De Gruyter
Год: 2022
Страниц: 174
Язык: английский
Формат: pdf (true), epub
Размер: 22.8 MB
Artificial Intelligence, Deep Learning, Machine Learning - whatever you’re doing if you don’t understand it - learn it. Because otherwise you’re going to be a dinosaur within 3 years. This book uses numerical analysis as the main tool to investigate methods in Machine Learning and AI. The e?ciency of neural network representation on for polynomial functions is studied in detail, together with an original description of the Latin hypercube method. In addition, unique features include the use of Tensorflow for implementation on session and the application n to the construction of new optimized numerical schemes.
Traditionally, the bread and butter in applied mathematics is the modeling of real phenomenons with partial differential equations and the numerical analysis of the corresponding discretized equations. An observation is that applied sciences and industry turn more and more in the direction of using Neural Networks, Machine Learning, and Deep Learning. This is clear at inspection of the exponentially growing number of publications on the coupling of Machine Learning and Neural Networks with computational fluid dynamics, modeling of turbulence, and many other problems.
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