Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics)

Chapmann, Joshua

ISBN 10: 1977662277 ISBN 13: 9781977662279

Editorial: CreateSpace Independent Publishing Platform, 2017

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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T�tulo

Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics)

Editor

CreateSpace Independent Publishing Platform

Fecha de publicaci�n

2017

Idioma

Ingl�s

ISBN 10

1977662277

ISBN 13

9781977662279

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

Neural Networks

Rese�a del editor

Why are engineers studying the human brain?

They are not doing it for fun, medical research or some form of global engineering competition. Engineers recognized that computers can process and store much more data than humans, yet even supercomputers can’t carry out tasks that the brain finds very simple such as facial recognition and natural language processing. MIT’s state-of-the-art research facility, named “Centre for Brains, Minds and Machines”, is a perfect testimonial to this fundamental interaction between the human brain and computers in today’s world.

Hence engineers began studying the processes and structures of our human brains, hoping to build a computer model of its functions – Neural Networks were born. These models are very simplistic, but fundamentally replicate on the inner structures of our own brains downright to the arrangement of individual brain cells, i.e. neurons.

In this book I show you exactly how engineers model the inner functions and structure of the human brain, covering the fundamental mathematical equations and underlying concepts. In particular you will learn:

How to Build a Computer model of a Brain Cell (or Neuron)
The Fundamental properties of a Neural Network
Multilayer Forward Networks
Using the Backpropagation algorithm to learn and adapt
Counter Propagation Networks
How to effectively train, validate and test a Neural network (avoiding overfitting)

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

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