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
Time Domain Electromagnetics (Academic Press Series in Engineering)
In recent times, we have seen increased interest in the direct time domain methods to calculate
electromagnetic scattering/interaction phenomenon. This may be due to the surge in activities
in the areas of EMP, short-pulse radar, or other related applications. It may also be due to the
fact that the time domain methods have several...
Introduction to the Geometry of Complex Numbers
The present work briefly develops the lectures which we have given since 1930 to the engineering candidates who chose the section of electromechanics at the Faculte polytechnique de Mons.
A memoir by Steinmetz 1 emphasized the simplifying role that can be played by the geometric interpretation of complex numbers in the...
Applied Calculus of Variations for Engineers
The subject of calculus of variations is to find optimal solutions to engineering problems where the optimum may be a certain quantity, a shape, or a function. Applied Calculus of Variations for Engineers addresses this very important mathematical area applicable to many engineering disciplines. Its unique,...
Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)
The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this book...
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