Fundamentals Of Neural Networks - Architectures, Algorithms And Applications | By Laurene V. Fausett | Revised Edition | Pearson Publication ( English Medium )

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Fundamentals Of Neural Networks - Architectures, Algorithms And Applications | By Laurene V. Fausett | Revised Edition | Pearson Publication ( English Medium ).

📖 Book Description:

This classic textbook by Laurene V. Fausett provides a clear, student-friendly introduction to neural networks, combining theory with practical applications. It is designed to help beginners understand the underlying principles and functioning of different neural network architectures.

The revised edition retains its focus on basic neural models, learning algorithms, and applications, but with refined examples and updated illustrations. The consistent organization of network architectures helps learners compare and contrast various models effectively. It places special emphasis on simple, illustrative problems that expose the strengths and weaknesses of each architecture.

🔑 Key Features:

✅ Comprehensive Architecture Coverage: Includes Hebbian learning, perceptrons, Adaline, multilayer networks with backpropagation, Kohonen maps, ART models, and Hopfield networks.

✅ Algorithm Emphasis: Algorithms are presented in a step-by-step format, ideal for programming or software simulation.

✅ Application-Driven Approach: Each neural network type is demonstrated using practical applications in pattern recognition, classification, and optimization.

✅ Educational Structure: Each chapter ends with review questions, exercises, and computer projects to reinforce learning.

✅ Unified Presentation Style: All network types are described using a standard format for easy comparison.

✅ Strong Mathematical Foundation: Explains the math behind neural operations, helping readers implement models correctly.

✅ Glossary and Index Included: Useful for quick revision and reference.

🧠 Topics Covered / Table of Contents Highlights:

Introduction to Neural Networks

Simple Neural Networks for Pattern Classification

Pattern Association Networks

Competitive Learning and Kohonen Maps

Adaptive Resonance Theory (ART)

Backpropagation and Multilayer Networks

Other Neural Networks (e.g., Hopfield, Boltzmann Machines)

Glossary, References, Index

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Product Details

SKU / BOOK Code:	Prsn-Fund-Of-Neu-Net-(E)
Publisher:	Pearson Publication
Author:	Laurene V. Fausett
Binding Type:	Paperback
No. of Pages:	480
ISBN-10:	9788131700532
ISBN-13:	978-8131700532
Edition:	Revised Edition
Language:	English Medium
Publish Year:	2025-05
Weight (g):	2800
Product Condition:	New
Reading Age:	Above 10 Years
Country of Origin:	India
Genre:	Textbooks & Study Guides
Manufacturer:	Pearson Publication
Importer:	Pearson Publication
Packer:	Fullfilled by Supplier

Product Description

Fundamentals Of Neural Networks - Architectures, Algorithms And Applications | By Laurene V. Fausett | Revised Edition | Pearson Publication ( English Medium ).

📖 Book Description:

This classic textbook by Laurene V. Fausett provides a clear, student-friendly introduction to neural networks, combining theory with practical applications. It is designed to help beginners understand the underlying principles and functioning of different neural network architectures.

The revised edition retains its focus on basic neural models, learning algorithms, and applications, but with refined examples and updated illustrations. The consistent organization of network architectures helps learners compare and contrast various models effectively. It places special emphasis on simple, illustrative problems that expose the strengths and weaknesses of each architecture.

🔑 Key Features:

✅ Comprehensive Architecture Coverage: Includes Hebbian learning, perceptrons, Adaline, multilayer networks with backpropagation, Kohonen maps, ART models, and Hopfield networks.

✅ Algorithm Emphasis: Algorithms are presented in a step-by-step format, ideal for programming or software simulation.

✅ Application-Driven Approach: Each neural network type is demonstrated using practical applications in pattern recognition, classification, and optimization.

✅ Educational Structure: Each chapter ends with review questions, exercises, and computer projects to reinforce learning.

✅ Unified Presentation Style: All network types are described using a standard format for easy comparison.

✅ Strong Mathematical Foundation: Explains the math behind neural operations, helping readers implement models correctly.

✅ Glossary and Index Included: Useful for quick revision and reference.

🧠 Topics Covered / Table of Contents Highlights:

Introduction to Neural Networks

Simple Neural Networks for Pattern Classification

Pattern Association Networks

Competitive Learning and Kohonen Maps

Adaptive Resonance Theory (ART)

Backpropagation and Multilayer Networks

Other Neural Networks (e.g., Hopfield, Boltzmann Machines)

Glossary, References, Index

Search Key - Fundamentals of Neural Networks Laurene Fausett, , Laurene Fausett neural networks book, , Neural network architectures explained, , Neural network learning algorithms, , Neural networks Fausett backpropagation, , Adaptive resonance theory neural net, , Kohonen self-organizing maps Fausett, , Pattern recognition neural network, , Perceptron algorithm Laurene Fausett, , Adaline neural network model, , Feedforward neural networks textbook, , Competitive learning neural networks, , Neural networks for beginners, , Neural networks in artificial intelligence, , Hopfield neural networks simplified, , Boltzmann machine neural networks, , Pattern classification with neural nets, , Neural network textbook for students, , Neural networks applications in AI, , Pearson neural network book, , Neural network computer science curriculum, , Neural network theory book, , Neural network models in depth, , Neural networks and pattern association, , Neural networks solved examples, , Neural network exercises for practice, , Neural networks architecture comparison, , Introduction to neural networks Fausett, , Learning rules in neural networks, , Self-organizing neural networks explained, ,  Fundamentals Of Neural Networks - Architectures, Algorithms And Applications | By Laurene V. Fausett | 1st Edition | Pearson Publication ( English Medium ), Publisher ‏ : ‎ Pearson Education India; 1st edition, Language ‏ : ‎ English, Paperback ‏ : ‎ 480 pages, ISBN-10 ‏ : ‎ 9788131700532, ISBN-13 ‏ : ‎ 978-8131700532, Fundamentals Of Neural Networks, Architectures, Algorithms, Applications, Computer Science, Engineering  Books, Engineering, Higher Studies, College Book, University Book, Higher Education.

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