Neural Networks: A Classroom Approach by Satish Kumar
Neural Networks: A Classroom Approach is a book by Satish Kumar, a professor of physics and computer science at Dayalbagh Educational Institute in India. The book covers the theory and applications of neural networks, a branch of artificial intelligence that mimics the learning process of the human brain. The book is intended for undergraduate and postgraduate students of engineering, computer science, and mathematics, as well as researchers and practitioners in the field.
The book consists of 16 chapters, divided into four parts. The first part introduces the basic concepts and models of artificial neurons, neural networks, and architectures. The second part deals with supervised learning algorithms, such as backpropagation, radial basis function networks, and support vector machines. The third part explores unsupervised learning algorithms, such as self-organizing maps, competitive learning, and adaptive resonance theory. The fourth part discusses advanced topics, such as recurrent neural networks, fuzzy neural networks, genetic algorithms, and neural network applications.
The book is written in a clear and concise style, with numerous examples, exercises, and illustrations. The book also provides MATLAB codes for implementing various neural network algorithms. The book aims to provide a comprehensive and practical guide for students and teachers of neural networks.One of the main features of the book is that it provides a historical perspective on the development of neural networks, tracing their origins from the early models of McCulloch and Pitts, Hebb, Rosenblatt, and Minsky and Papert, to the modern models of Hopfield, Rumelhart and Hinton, Kohonen, Grossberg, and others. The book also compares and contrasts neural networks with other computational paradigms, such as symbolic AI, fuzzy logic, and evolutionary computing.
The book is designed to be self-contained and accessible for readers with a basic knowledge of calculus, linear algebra, probability, and statistics. The book also provides appendices on MATLAB programming, matrix operations, optimization techniques, and numerical methods. The book is accompanied by a CD-ROM that contains the MATLAB codes, data sets, and solutions to selected exercises.
The book has received positive reviews from students and teachers who have used it as a textbook or a reference book for neural networks courses. The book has been praised for its clarity, depth, coverage, and pedagogy. The book is also suitable for self-study and professional development for anyone interested in learning more about neural networks and their applications.Some of the topics covered in the book include:
Artificial neuron models and activation functions
Feedforward neural networks and multilayer perceptrons
Backpropagation algorithm and its variants
Radial basis function networks and kernel methods
Support vector machines and kernel machines
Recurrent neural networks and dynamical systems
Hopfield networks and associative memory
Boltzmann machines and simulated annealing
Self-organizing maps and Kohonen networks
Competitive learning and vector quantization
Adaptive resonance theory and artmap networks
Fuzzy neural networks and fuzzy inference systems
Genetic algorithms and genetic programming
Neural network applications in pattern recognition, image processing, speech recognition, data mining, control systems, robotics, and bioinformatics
The book also provides a comprehensive bibliography of over 800 references on neural networks and related fields. The book is a valuable resource for anyone who wants to learn about the theory and practice of neural networks. 061ffe29dd