sc JNTUH B.TECH R18 4-1 Syllabus For artificial neural networks PDF 2022 – Cynohub


JNTUH B.TECH R18 4-1 Syllabus For artificial neural networks PDF 2022


JNTUH B.TECH R18 4-1 Syllabus For artificial neural networks PDF 2022

Get Complete Lecture Notes for artificial neural networks on Cynohub APP

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You will be able to find information about artificial neural networks along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding artificial neural networks after reading this blog. artificial neural networks has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. artificial neural networks can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to artificial neural networks are mentioned below in detail. If you are having a hard time understanding artificial neural networks or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

artificial neural networks Unit One


Introduction: A Neural Network, Human Brain, Models of a Neuron, Neural Networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks
Learning Process: Error Correction Learning, Memory Based Learning, Hebbian Learning, Competitive, Boltzmann Learning, Credit Assignment Problem, Memory, Adaption, Statistical Nature of the Learning Process

artificial neural networks Unit Two


Single Layer Perceptrons: Adaptive Filtering Problem, Unconstrained Organization Techniques, Linear Least Square Filters, Least Mean Square Algorithm, Learning Curves, Learning Rate Annealing Techniques, Perceptron –Convergence Theorem, Relation Between Perceptron and Bayes Classifier for a Gaussian Environment
Multilayer Perceptron: Back Propagation Algorithm XOR Problem, Heuristics, Output Representation and Decision Rule, Computer Experiment, Feature Detection

Get Complete Lecture Notes for artificial neural networks on Cynohub APP

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artificial neural networks Unit Three


Back Propagation: Back Propagation and Differentiation, Hessian Matrix, Generalization, Cross Validation, Network Pruning Techniques, Virtues and Limitations of Back Propagation Learning, Accelerated Convergence, Supervised Learning

artificial neural networks Unit Four


Self-Organization Maps (SOM): Two Basic Feature Mapping Models, Self-Organization Map, SOM Algorithm, Properties of Feature Map, Computer Simulations, Learning Vector Quantization, Adaptive Patter Classification

artificial neural networks Unit Five


Neuro Dynamics: Dynamical Systems, Stability of Equilibrium States, Attractors, Neuro Dynamical Models, Manipulation of Attractors as a Recurrent Network Paradigm
Hopfield Models – Hopfield Models, restricted boltzmen machine.

artificial neural networks Course Objectives

To understand the biological neural network and to model equivalent neuron models.
To understand the architecture, learning algorithms
To know the issues of various feed forward and feedback neural networks.
To explore the Neuro dynamic models for various problems.

artificial neural networks Course Outcomes

Upon completing this course, the student will be able to
Understand the similarity of Biological networks and Neural networks
Perform the training of neural networks using various learning rules.
Understanding the concepts of forward and backward propagations.
Understand and Construct the Hopfield models.

artificial neural networks Text Books

Neural Networks a Comprehensive Foundations, Simon S Haykin, PHI Ed.,.
Introduction to Artificial Neural Systems Jacek M. Zurada, JAICO Publishing House Ed. 2006.

artificial neural networks Reference Books

Neural Networks in Computer Inteligance, Li Min Fu TMH 2003
Neural Networks -James A Freeman David M S Kapura Pearson Ed., 2004.
Artificial Neural Networks – B. Vegnanarayana Prentice Hall of India P Ltd 2005

Scoring Marks in artificial neural networks

Scoring a really good grade in artificial neural networks is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your examinations . This video will also inform students on how to score high grades in artificial neural networks. There are a lot of reasons for getting a bad score in your artificial neural networks exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTUH B.Tech R18 artificial neural networks was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

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Comments (4)






    i need some documents reletade to acadamic years


    notes download

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