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Neural Networks with R

You're reading from  Neural Networks with R

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Pages 270 pages
Edition 1st Edition
Languages
Authors (2):
Balaji Venkateswaran Balaji Venkateswaran
Profile icon Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
View More author details
Toc

Table of Contents (14) Chapters close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Neural network learning algorithm optimization


The procedure used to carry out the learning process in a neural network is called the training algorithm. The learning algorithm is what the machine learning algorithm chooses as model with the best optimization. The aim is to minimize the loss function and provide more accuracy. Here we illustrate some of the optimization techniques, other than gradient descent.

The Particle Swarm Optimization (PSO) method is inspired by observations of social and collective behavior on the movements of bird flocks in search of food or survival. It is similar to a fish school trying to move together. We know the position and velocity of the particles, and PSO aims at searching a solution set in a large space controlled by mathematical equations on position and velocity. It is bio-inspired from biological organism behavior for collective intelligence.

Simulated annealing is a method that works on a probabilistic approach to approximate the global optimum for...

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