Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from th

Description: Deep Neuro-Fuzzy Systems with Python by Himanshu Singh, Yunis Ahmad Lone Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python.Youll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. Youll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them.In the last section of the book youll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. Youll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What Youll LearnUnderstand fuzzy logic, membership functions, fuzzy relations, and fuzzy inferenceReview neural networks, back propagation, and optimizationWork with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. Youll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. Youll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book youll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. Youll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. Author Biography Himanshu Singh is currently a Consultant to Artificial Intelligence for ADP Inc. with over 5 years of experience in the AI industry, primarily in Computer Vision and Natural Language Processing. Himanshu has authored three books on Machine Learning. He has an MBA from Narsee Monjee Institute of Management Studies, and a postgraduate diploma in Applied Statistics. Yunis Ahmad Lone has over 22 years of experience in the IT industry, has been involved with Machine Learning for 10 years. Currently, Yunis is a PhD researcher at Trinity College, Dublin, Ireland. Yunis completed his Bachelors and Masters both from BITS Pilani, and worked on various leadership positions in MNCs like Tata Consultancy Services, Deloitte, and Fidelity Investments. Table of Contents Chapter 1: Introduction to Fuzzy Set Theory.- Chapter 2: Fuzzy Rules and Reasoning .- Chapter 3: Fuzzy Inference Systems.- Chapter 4: Introduction to Machine Learning.- Chapter 5: Artificial Neural Networks.- Chapter 6: Fuzzy Neural Networks.- Chapter 7: Advanced Fuzzy Networks. Feature Explains deep neuro-fuzzy systems with applications and mathematical details Implementations of all the applications using Python Covers the recent applications of neuro fuzzy inference systems in industry Details ISBN1484253604 Author Yunis Ahmad Lone Publisher APress Edition 1st ISBN-10 1484253604 ISBN-13 9781484253601 Format Paperback Imprint APress Place of Publication Berkley Country of Publication United States DEWEY 005.133 Subtitle With Case Studies and Applications from the Industry Year 2019 Pages 260 Publication Date 2019-12-01 Short Title Deep Neuro-Fuzzy Systems with Python Language English DOI 10.1007/978-1-4842-5361-8 UK Release Date 2019-12-01 AU Release Date 2019-12-01 NZ Release Date 2019-12-01 US Release Date 2019-12-01 Illustrations 143 Illustrations, black and white; XV, 260 p. 143 illus. Edition Description 1st ed. Alternative 9781484267288 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. 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