Neural Networks For Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

 

Neural Networks For  Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

Neural Networks For Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

Neural Networks For  Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

Introduction:

Starting with an early on discourse on the part of neural systems in exploratory information investigation, this book gives a strong establishment of essential neural system ideas. It contains an outline of neural system architectures for down to earth information examination took after by broad orderly scope on straight systems, and also, multi-layer perceptron for nonlinear forecast and arrangement clarifying all phases of preparing and show improvement represented through reasonable cases and contextual investigations. Later parts display a broad scope on Self Organizing Maps for nonlinear information grouping, intermittent systems for direct nonlinear time arrangement estimating, and other system sorts suitable for experimental information investigation.

With a straightforward arrangement utilizing broad graphical outlines and multidisciplinary exploratory setting, this book fills the hole in the business sector for neural systems for multi-dimensional investigative information, and relates neural systems to measurements.

Contents:

1 From Data to Models: Complexity and Challenges in Understanding Biological, Ecological, and Regular Systems ……………………………………………………………………… 1

1.1: Introduction 1

1.2: Layout of the Book 4

References 7

2 Fundamentals of Neural Networks and Models for Linear Data Analysis ………………………………………………………. 11

2.1: Introduction and Overview 11

2.2: Neural Networks and Their Capabilities 12

2.3: Inspirations from Biology 16

3 Neural Networks for Nonlinear Pattern Recognition ………….. 69

3.1: Overview and Introduction 69

3.1.1: Multilayer Perceptron 71

3.2: Nonlinear Neurons 72

3.2.1: Neuron Activation Functions 73

4 Learning of Nonlinear Patterns by Neural Networks ………… 113

4.1: Introduction and Overview 113

4.2: Supervised Training of Networks for Nonlinear

Design Recognition 114

4.3: Gradient Descent and Error Minimization 115

5 Implementation of Neural Network Models for Removing Reliable Patterns from Data ……………………………… 195

5.1: Introduction and Overview 195

5.2: Bias–Variance Tradeoff 196

5.3: Improving Generalization of Neural Networks 197

6 Data Exploration, Dimensionality Reduction, what’s more, Feature Extraction……………………………………………………….. 245

6.1: Introduction and Overview 245

6.1.1: Example: Thermal Conductivity of Wood in Relation

to Correlated Input Data 247

6.2: Data Visualization 248

6.2.1: Correlation Scatter Plots and Histograms 248

7 Assessment of Uncertainty of Neural Network Models Using Bayesian Statistics………………………………………… 283

7.1: Introduction and Overview 283

7.2: Estimating Weight Uncertainty Using Bayesian Statistics 285

7.2.1: Quality Criterion 285

7.2.2: Incorporating Bayesian Statistics to Estimate

Weight Uncertainty 2

Neural Networks For  Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

 

Neural Networks For Applied Sciences and Engineering by Sandhya Samarasinghe eBook Free Download

 

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