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portada Neural Networks for Complete Beginners: Introduction for Neural Network Programming
Type
Physical Book
Language
English
Format
Paperback
Dimensions
22.9 x 15.2 x 0.5 cm
Weight
0.14 kg.
ISBN13
9781543268720

Neural Networks for Complete Beginners: Introduction for Neural Network Programming

Mark Smart (Author) · Createspace Independent Publishing Platform · Paperback

Neural Networks for Complete Beginners: Introduction for Neural Network Programming - Smart, Mark

New Book

£ 15.13

  • Condition: New
Origin: U.S.A. (Import costs included in the price)
It will be shipped from our warehouse between Thursday, June 27 and Monday, July 15.
You will receive it anywhere in United Kingdom between 1 and 3 business days after shipment.

Synopsis "Neural Networks for Complete Beginners: Introduction for Neural Network Programming"

This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural networks. The first part of the book is an overview of artificial neural networks so as to help the reader understand what they are. You will also learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the concept of learning which is common in human beings. This book guides you to understand how learning takes place in artificial neural networks. The back-propagation algorithm, which is used for training artificial neural networks, is discussed. The book also guides you through the architecture of an artificial neural network. The various types of artificial neural networks based on their architecture are also discussed. The book guides you on the necessary steps for one to build a neural network. The perception, which is a type of an artificial neural network, is explored, and you will explore how to implement one programmatically. The following topics are discussed in this book: -What is a Neural Network? -Learning in Neural Networks -The Architecture of Neural Networks -Building Neural Networks -The Perceptron

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