



  R Deep Learning Essentials
Key Features

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...
  

CosmologyThis book is unique in the detailed, selfcontained, and comprehensive treatment that it gives to the ideas and formulas that are used and tested in modern cosmological research. It divides into two parts, each of which provides enough material for a onesemester graduate course. The first part deals chiefly with the isotropic and homogeneous...     

Mathematics for Electrical Engineering and ComputingMathematics for Electrical Engineering and Computing embraces many applications of modern mathematics, such as Boolean Algebra and Sets and Functions, and also teaches both discrete and continuous systems  particularly vital for Digital Signal Processing (DSP). In addition, as most modern engineers are required to study software,...   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 realworld problems, computational linguistics, mathematical models and methods for meteorology, earth systems,...   Ordinary Differential EquationsSkillfully 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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