Concepts of Metaheuristics

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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 "

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...

Training algorithms for Neural Networks from Mrinmoy Majumder A tutorial on Conjugate Gradient Descent and Newton's Method.Go through the PPT and see if you can understand the concept and apply the same.If not do reply me.