R Deep Learning Essentials
Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
Build models relating to neural
Mathematical Modelling, Optimization, Analytic and Numerical Solutions (Industrial and Applied Mathematics)
This book discusses a variety of topics related to industrial and applied mathematics, focusing on wavelet theory, sampling theorems, inverse problems and their applications, partial differential equations as a model of real-world problems, computational linguistics, mathematical models and methods for meteorology, earth systems,...
Ordinary Differential Equations Skillfully organized introductory text examines origin of differential equations, then defines basic terms and outlines general solution of a differential equation. Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton’s Interpolation Formulas,...
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