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This book uses numerical analysis as the main tool to investigate methods in machine learning and A.I. The efficiency 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.
Introduction.
Acknowledgement.
Objective functions, neural networks, and linear algebra.
Approximation properties.
A functional equation.
Datasets.
Stochastic gradient methods.
Examples and research in the field.
Index