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portada Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics) (en Inglés)
Formato
Libro Físico
Editorial
Idioma
Inglés
N° páginas
108
Encuadernación
Tapa Blanda
ISBN13
9781977662279

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

Joshua Chapmann (Autor) · Createspace · Tapa Blanda

Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics) (en Inglés) - Joshua Chapmann

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Reseña del libro "Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics) (en Inglés)"

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 NetworksUsing the Backpropagation algorithm to learn and adaptCounter Propagation NetworksHow to effectively train, validate and test a Neural network (avoiding overfitting)

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