Posts

Introduction to Artificial Neural Networks

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This video explains the concept of Artificial Neural Networks in a very simple way. "Artificial neural networks, usually simply called neural networks, are computing systems inspired by the biological neural networks that constitute animal brains. "(Wiki). This tutorial will help you learn the basic concept and how to apply ANN to solve simple problems with examples. This tutorial is a part of a course on " Introduction to Artificial Neural Networks "

A Simple Tutorial on Analytical Hierarchy Process

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An introduction to the most used multi-criteria decision-making method including example. The tutorial uses a graphical mode to explain the concept.  The Analytic Hierarchy Process (AHP) is a method for organizing and analyzing complex decisions, using math and psychology. It was developed by Thomas L. Saaty in the 1970s and has been refined since then.( Passage Technology ). See the entire video as given above. If you are interested to know more about the technique then click here Music Courtesy: https://pixabay.com/ If you want more such tutorial visit: http://www.baipatra.ws If you have a paper to publish then consider the journals here: http://energyinstyle.website/  

Solving Quadratic Optimization Problems: KKT Conditions

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A tutorial on solving Quadratic Optimization Problems

Classification of Optimization Techniques

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Classification of Optimization Techniques full tutorial at http://www.baipatra.ws  

Constraints in Optimization Techniques

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Constraints in Optimization Techniques Full Tutorial: http://www.baipatra.ws

Fundamentals of Optimization Techniques

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Fundamentals of Optimization Techniques Full Tutorial at http://www.baipatra.ws

Distribution Function : Prequisite to Model Development by ANN

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A brief introduction to distribution function which depicts the pattern of data. This information is essential to select the type of ANN models that can be applied to develop a model based on that data. Access the complete course at https://gum.co/Ksjlq or find such videos at http://www.baipatra.ws Music by Palle1958 from https://pixabay.com/

Tutorial on Auto-and Cross Regression Models : Prerequiste to ANN model development

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Auto and Cross Regression Models

Auto and Cross Correlation as a prerequisite to ANN model development

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Auto and Cross-Correlation

Learn Linear Programming with Examples

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Lear about the concepts of Linear Programming 1)How to formulate the problem ? 2)How to solve the problem by the graphical method?

Learn Dynamic Programming and Recursive Equation with Example

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Learn about Dynamic Programming with Examples.

Learn Genetic Algorithm with Example

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A simple tutorial on GA. Explained with example.

Five most widely used algorithms for training neural networks

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The procedure used to carry out the learning process in a neural network is called the optimization algorithm (or optimizer). There are many different optimization algorithms. All have different characteristics and performance in terms of memory requirements, processing speed, and numerical precision. Four major parameters are estimated in the process of developing neural network-based models. The four significant parameters of neural networks include : 1)Activation function from Input to Hidden 2)Activation function from Hidden to Output 3)Number of hidden layers 4)The magnitude of weights of the connections (To know more about the above parameters see my tutorial on Artificial Neural Network ) This article is about the methods utilized to estimate the weights of the connections. The process of estimation of weights is similar to optimization problems. Here the weights are design variables. The transfer function prepared to transfer the information from input to output is the objectiv