JNTUK R16 4-2 Artificial Neural Networks Material PDF Download
Students those who are studying JNTUK R16 CSE Branch, Can Download Unit wise R16 4-2 Artificial Neural Networks (ANN) Material/Notes PDFs below.
JNTUK R16 4-2 Artificial Neural Networks Material PDF Download
OBJECTIVES:
- Understand the role of neural networks in engineering, artificial intelligence, and cognitive modeling.
- Provide knowledge of supervised learning in neural networks
- Provide knowledge of computation and dynamical systems using neural networks
- Provide knowledge of reinforcement learning using neural networks.
- Provide knowledge of unsupervised learning using neural networks.
- Provide hands-on experience in selected applications
UNIT-1
Introduction and ANN Structure. Biological neurons and artificial neurons. Model of an ANN. Activation functions used in ANNs. Typical classes of network architectures.
UNIT-2
Mathematical Foundations and Learning mechanisms.Re-visiting vector and matrix algebra. State-space concepts. Concepts of optimization. Error-correction learning. Memory-based learning. Hebbian learning. Competitive learning.
UNIT-3
Single layer perceptrons. Structure and learning of perceptrons. Pattern classifier – introduction and Bayes’ classifiers. Perceptron as a pattern classifier. Perceptron convergence. Limitations of a perceptrons.
UNIT-4
Feed forward ANN. Structures of Multi-layer feed forward networks. Back propagation algorithm. Back propagation – training and convergence. Functional approximation with back propagation. Practical and design issues of back propagation learning.
UNIT-5
Radial Basis Function Networks. Pattern separability and interpolation. Regularization Theory. Regularization and RBF networks.RBF network design and training. Approximation properties of RBF.
UNIT-6
Support Vector machines. Linear separability and optimal hyperplane.Determination of optimal hyperplane. Optimal hyperplane for nonseparable patterns.Design of an SVM.Examples of SVM.
TEXT BOOKS:
- Simon Haykin, “Neural Networks: A comprehensive foundation”, Second Edition, Pearson Education Asia.
- Satish Kumar, “Neural Networks: A classroom approach”, Tata McGraw Hill, 2004.
REFERENCE BOOKS:
- Robert J. Schalkoff, “Artificial Neural Networks”, McGraw-Hill International Editions, 1997.
OUTCOMES:
- This course has been designed to offer as a graduate-level/ final year undergraduate level elective subject to the students of any branch of engineering/ science, having basic foundations of matrix algebra, calculus and preferably (not essential) with a basic knowledge of optimization.
- Students and researchers desirous of working on pattern recognition and classification, regression and interpolation from sparse observations; control and optimization are expected to find this course useful. The course covers theories and usage of artificial neural networks (ANN) for problems pertaining to classification (supervised/ unsupervised) and regression.
- The course starts with some mathematical foundations and the structures of artificial neurons, which mimics biological neurons in a grossly scaled down version. It offers mathematical basis of learning mechanisms through ANN. The course introduces perceptrons, discusses its capabilities and limitations as a pattern classifier and later develops concepts of multilayer perceptrons with back propagation learning.
