Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks

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)

Book details

  • Publisher:CreateSpace Independent Publishing Platform
  • Publication date:September 26, 2017
  • ISBN-10:1977662277
  • ISBN-13:978-1977662279
  • Pages:54 pages
  • Format:epub
  • Size:0.36Mb
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