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Add to basketPaperback. Condition: new. Paperback. 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) This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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ISBN 10: 1978304870 ISBN 13: 9781978304871
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Add to basketPaperback. Condition: new. Paperback. What is a MEMORYLESS predictive model? Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of "memorylessness", stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored. This model construction may sound overly simplistic. After all, if you have historical data why not use it to develop more complete and well-informed models? Surely, it would lead to more accurate predictions. However, when modelling time-series data where previous results are of limited relevance, a memoryless model delivers vast performance advantages. By considering only the present state, algorithms become highly scalable, stable, fast and, above-all-else, extremely versatile. Speech recognition is a perfect example - nearly all of today's speech recognition algorthms are built using Markov Models. In this book we will explore why a Memoryless predictive model can be so advantageous to the modern tech industry. We will take a look at fundamental mathematics and high-level concepts alike, extending our understanding of the subject beyond the simple Markov Model. You will learn. Foundations of Markov ModelsMarkov ChainsCase Study: Google PageRankHidden Markov ModelsBayesian NetworksInference Tasks This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.