Neural Networks A Classroom Approach By Satish Kumar Pdf Free 11
Neural Networks: A Classroom Approach by Satish Kumar
Neural networks are computational models that mimic the structure and function of biological neurons and their connections. They are widely used for various tasks such as pattern recognition, data mining, natural language processing, computer vision, and artificial intelligence. Neural networks can learn from data and adapt to changing environments, making them powerful and flexible tools for solving complex problems.
One of the most comprehensive and accessible books on neural networks is Neural Networks: A Classroom Approach by Satish Kumar. This book covers the theory and practice of neural networks in a clear and systematic way, with numerous examples, exercises, and case studies. The book is suitable for undergraduate and postgraduate students of computer science, engineering, mathematics, physics, and other disciplines, as well as for researchers and professionals who want to learn about neural networks.
What does the book cover?
The book consists of 11 chapters, each focusing on a different aspect of neural networks. The chapters are as follows:
Chapter 1: Introduction: This chapter provides an overview of neural networks, their history, applications, advantages, and limitations. It also introduces some basic concepts and terminology used in neural network literature.
Chapter 2: Biological Neurons and Neural Networks: This chapter describes the structure and function of biological neurons and their networks. It also explains how artificial neurons and neural networks are inspired by biological systems.
Chapter 3: Artificial Neurons, Neural Networks and Architectures: This chapter presents the mathematical models of artificial neurons and neural networks. It also discusses the different types of network architectures, such as feedforward, recurrent, modular, and hierarchical.
Chapter 4: Activation Functions: This chapter explains the role and properties of activation functions in neural networks. It also describes some common activation functions, such as linear, threshold, sigmoidal, hyperbolic tangent, radial basis, Gaussian, etc.
Chapter 5: Supervised Learning I: Perceptrons and LMS: This chapter introduces the concept of supervised learning in neural networks. It also covers two important supervised learning algorithms: the perceptron learning rule and the least mean square (LMS) algorithm.
Chapter 6: Supervised Learning II: Backpropagation Algorithm: This chapter explains the backpropagation algorithm, which is one of the most widely used supervised learning algorithms for multilayer feedforward neural networks. It also discusses some variations and extensions of the backpropagation algorithm, such as momentum term, adaptive learning rate, weight decay, etc.
Chapter 7: Supervised Learning III: Radial Basis Function Networks: This chapter describes radial basis function (RBF) networks, which are a special type of feedforward neural networks that use radial basis functions as activation functions. It also covers some aspects of RBF network design, such as center selection, width determination, weight optimization, etc.
Chapter 8: Unsupervised Learning I: Competitive Learning: This chapter introduces the concept of unsupervised learning in neural networks. It also covers some competitive learning algorithms, such as winner-take-all (WTA), k-means clustering, learning vector quantization (LVQ), self-organizing maps (SOM), etc.
Chapter 9: Unsupervised Learning II: Hebbian Learning: This chapter explains Hebbian learning, which is a biologically inspired unsupervised learning rule that strengthens the synaptic connections between coactive neurons. It also discusses some applications of Hebbian learning, such as principal component analysis (PCA), independent component analysis (ICA), etc.
Chapter 10: Associative Memory Networks: This chapter describes associative memory networks, which are a class of neural networks that can store and recall patterns based on partial or noisy inputs. It also covers some types of associative memory networks, such as autoassociative memory (AAM), heteroassociative memory (HAM), bidirectional associative memory (BAM), Hopfield network (HN), etc.
Chapter 11: Neural Network Applications: This chapter presents some examples of neural network applications in various domains, such as character recognition, face detection, speech recognition, text classification, image compression, medical diagnosis, stock market prediction, etc.
How to get the book?
The book is published by McGraw-Hill Education (India) Pvt Limited in 2004. It has 736 pages and ISBN 0070482926. The book is available in both print and digital formats. You can buy the book from online or offline bookstores, or you can download the PDF version for free from [this link].
If you are interested in learning about neural networks, Neural Networks: A Classroom Approach by Satish Kumar is a great resource that will help you understand the concepts and techniques of this fascinating field.