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Principles of soft computing using Python programming : learn how to deploy soft computing models in real world applications

Nandi, Gypsy, author.

Principles of soft computing using Python programming : learn how to deploy soft computing models in real world applications

Nandi, Gypsy, author.

9781394173150

9781394173143

9781394173167

First edition.

1 online resource

Includes index.

Cover -- Title Page -- Copyright -- Contents -- About the Author -- Preface -- Chapter 1 Fundamentals of Soft Computing -- 1.1 Introduction to Soft Computing -- 1.2 Soft Computing versus Hard Computing -- 1.3 Characteristics of Soft Computing -- 1.4 Components of Soft Computing -- 1.4.1 Fuzzy Computing -- 1.4.2 Neural Network -- 1.4.3 Evolutionary Computing -- 1.4.4 Machine Learning -- 1.4.5 Other Techniques of Soft Computing -- Exercises -- Chapter 2 Fuzzy Computing -- 2.1 Fuzzy Sets -- 2.1.1 Features of Fuzzy Membership Functions -- 2.2 Fuzzy Set Operations -- 2.3 Fuzzy Set Properties

2.4 Binary Fuzzy Relation -- 2.5 Fuzzy Membership Functions -- 2.6 Methods of Membership Value Assignments -- 2.7 Fuzzification vs. Defuzzification -- 2.8 Fuzzy c-Means -- Exercises -- Chapter 3 Artificial Neural Network -- 3.1 Fundamentals of Artificial Neural Network (ANN) -- 3.2 Standard Activation Functions in Neural Networks -- 3.2.1 Binary Step Activation Function -- 3.2.2 Linear Activation Function -- 3.2.3 Sigmoid/Logistic Activation Function -- 3.2.4 ReLU Activation Function -- 3.2.5 Tanh Activation Function -- 3.2.6 Leaky ReLU Activation Function -- 3.2.7 SoftMax Activation Function

3.3 Basic Learning Rules in ANN -- 3.3.1 Hebbian Learning Rule -- 3.3.2 Perceptron Learning Rule -- 3.3.3 Delta Learning Rule -- 3.3.4 Correlation Learning Rule -- 3.3.5 Competitive Learning Rule -- 3.3.6 Outstar Learning Rule -- 3.4 McCulloch-Pitts ANN Model -- 3.5 Feed-Forward Neural Network -- 3.5.1 Single-Layer Perceptron -- 3.5.2 Multilayer Perceptron -- 3.5.3 Radial Basis Function Network -- 3.6 Feedback Neural Network -- 3.6.1 Self-Organizing Map (SOM) -- 3.6.2 Hopfield Neural Network (HNN) -- Exercises -- Chapter 4 Deep Learning -- 4.1 Introduction to Deep Learning

4.2 Classification of Deep Learning Techniques -- 4.2.1 Convolutional Neural Networks -- 4.2.2 Recurrent Neural Network (RNN) -- 4.2.3 Generative Adversarial Network (GAN) -- 4.2.4 Autoencoders -- Exercises -- Chapter 5 Probabilistic Reasoning -- 5.1 Introduction to Probabilistic Reasoning -- 5.1.1 Random Experiment -- 5.1.2 Random Variables -- 5.1.3 Independence -- 5.1.4 Sample Space -- 5.1.5 Odds and Risks -- 5.1.6 Expected Values -- 5.2 Four Perspectives on Probability -- 5.2.1 The Classical Approach -- 5.2.2 The Empirical Approach -- 5.2.3 The Subjective Approach

5.2.4 The Axiomatic Approach -- 5.3 The Principles of Bayesian Inference -- 5.4 Belief Network and Markovian Network -- 5.4.1 Syntax and Semantics -- 5.4.1.1 Belief Network -- 5.4.1.2 Markovian Network -- 5.4.2 Conditional Independence -- 5.4.3 Learning Methods of the Networks -- 5.5 Hidden Markov Model -- 5.6 Markov Decision Processes -- 5.7 Machine Learning and Probabilistic Models -- Exercises -- Chapter 6 Population-Based Algorithms -- 6.1 Introduction to Genetic Algorithms -- 6.2 Five Phases of Genetic Algorithms -- 6.2.1 Population Initialization

"Soft computing is an innovative approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision. Soft computing techniques have the roots in machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. Hence, it is a vital tool used for performing several computing operations. Often it has been noticed that no fixed solution can be found for a computationally hard task. In such a case, a precisely stated analytical model may not work to produce precise results. For this, the soft computing approach can be used that does not require a fixed mathematical modelling for problem solving. In fact, the uniqueness and strength of soft computing lie in its superpower of fusing of two or more soft computing computational models/techniques to generate optimum results"-- Provided by publisher.

John Wiley and Sons

Soft computing.

Python (Computer program language)

Programming (Computers)

Informatique douce.

Python (Langage de programmation)

Soft computing

https://onlinelibrary.wiley.com/doi/book/10.1002/9781394173167