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An Introduction to Neural Networks

An Introduction to Neural Networks
Prof. Leslie Smith Centre for Cognitive and Computational Neuroscience Department of Computing and Mathematics University of Stirling. lss@cs.stir.ac.uk last major update: 25 October 1996: minor update 22 April 1998 and 12 Sept 2001: links updated (they were out of date) 12 Sept 2001; fix to math font (thanks Sietse Brouwer) 2 April 2003 This document is a roughly HTML-ised version of a talk given at the NSYN meeting in Edinburgh, Scotland, on 28 February 1996, then updated a few times in response to comments received. Please email me comments, but remember that this was originally just the slides from an introductory talk! What is a neural network? Some algorithms and architectures. Where have they been applied? What new applications are likely? Some useful sources of information. Some comments added Sept 2001 NEW: questions and answers arising from this tutorial Why would anyone want a `new' sort of computer? What are (everyday) computer systems good at... .....and not so good at? Good at Related:  Neural Network

Google scientist Jeff Dean on how neural networks are improving everything Google does Simon Dawson Google's goal: A more powerful search that full understands answers to commands like, "Book me a ticket to Washington DC." Jon Xavier, Web Producer, Silicon Valley Business Journal If you've ever been mystified by how Google knows what you're looking for before you even finish typing your query into the search box, or had voice search on Android recognize exactly what you said even though you're in a noisy subway, chances are you have Jeff Dean and the Systems Infrastructure Group to thank for it. As a Google Research Fellow, Dean has been working on ways to use machine learning and deep neural networks to solve some of the toughest problems Google has, such as natural language processing, speech recognition, and computer vision. Q: What does your group do at Google? A: We in our group are trying to do several things. |View All

A Non-Mathematical Introduction to Using Neural Networks The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is. Most laypeople think of neural networks as a sort of artificial brain. Neural networks are one small part of AI. The human brain really should be called a biological neural network (BNN). There are some basic similarities between biological neural networks and artificial neural networks. Like I said, neural networks are designed to accomplish one small task. The task that neural networks accomplish very well is pattern recognition. Figure 1: A Typical Neural Network As you can see, the neural network above is accepting a pattern and returning a pattern. Neural Network Structure Neural networks are made of layers of similar neurons. Conclusion

Intro to Neural Networks Classification | Frontline Systems Artificial neural networks are relatively crude electronic networks of "neurons" based on the neural structure of the brain. They process records one at a time, and "learn" by comparing their classification of the record (which, at the outset, is largely arbitrary) with the known actual classification of the record. The errors from the initial classification of the first record is fed back into the network, and used to modify the networks algorithm the second time around, and so on for many iterations. Roughly speaking, a neuron in an artificial neural network is A set of input values (xi) and associated weights (wi)A function (g) that sums the weights and maps the results to an output (y). The input layer is composed not of full neurons, but rather consists simply of the values in a data record, that constitute inputs to the next layer of neurons. Training an Artificial Neural Network The Iterative Learning Process Note that some networks never learn. Feedforward, Back-Propagation

IBM Research creates new foundation to program SyNAPSE chips (Credit: IBM Research) Scientists from IBM unveiled on Aug. 8 a breakthrough software ecosystem designed for programming silicon chips that have an architecture inspired by the function, low power, and compact volume of the brain. The technology could enable a new generation of intelligent sensor networks that mimic the brain’s abilities for perception, action, and cognition. Dramatically different from traditional software, IBM’s new programming model breaks the mold of sequential operation underlying today’s von Neumann architectures and computers. It is instead tailored for a new class of distributed, highly interconnected, asynchronous, parallel, large-scale cognitive computing architectures. “Architectures and programs are closely intertwined and a new architecture necessitates a new programming paradigm,” said Dr. “We are working to create a FORTRAN [a pioneering computer language] for synaptic computing chips. Paving the Path to SyNAPSE Take the human eyes, for example.

PyBrain Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. Neural networks approach the problem in a different way. and then develop a system which can learn from those training examples. In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. Perceptrons What is a neural network? So how do perceptrons work? is a shorthand.

Universe Grows Like A giant Brain The universe may grow like a giant brain, according to a new computer simulation. The results, published Nov.16 in the journal Nature's Scientific Reports, suggest that some undiscovered, fundamental laws may govern the growth of systems large and small, from the electrical firing between brain cells and growth of social networks to the expansion of galaxies. "Natural growth dynamics are the same for different real networks, like the Internet or the brain or social networks," said study co-author Dmitri Krioukov, a physicist at the University of California San Diego. The new study suggests a single fundamental law of nature may govern these networks, said physicist Kevin Bassler of the University of Houston, who was not involved in the study. "At first blush they seem to be quite different systems, the question is, is there some kind of controlling laws can describe them?" By raising this question, "their work really makes a pretty important contribution," he said. Similar Networks

Connectivism et enaction...mon cheminement Quand j'ai commencé à travailler sur le concept d'énaction de Francisco Varela, il y a eu un moment de profonds questionnements pour moi...j'ai eu le sentiment que les repères sur lesquels je m'appuyais tombaient les uns après les autres...un peu comme si je vacillais mentalement...presque physiquement d'ailleurs...impossible de dormir pendant près de deux semaines ! Ce qui émergeait pour moi à ce moment là, c'était l'idée qu'aucun modèle pré-existant n'est indispensable à la construction de mes propres représentations....c'était l'idée que l'on peut apprendre de façon autonome dans un couplage permanent au monde...coup de tonnerre dans mon ciel ! Cette idée s'imposait comme une évidence et tous mes repérages se déplaçaient et prenaient sens autour de cette approche...je ne maîtrisais rien et cela se faisait...il faut dire aussi que ce concept résonnait largement avec ma pratique et trouvait là sa cohérence ! Je me suis remise à dormir ! A lire en parallèle :

Deep Learning and Neural Networks Advanced Research Seminar I/III Graduate School of Information Science Nara Institute of Science and Technology January 2014 Instructor: Kevin Duh, IS Building Room A-705 Office hours: after class, or appointment by email (x@is.naist.jp where x=kevinduh) Course Description Deep Learning is a family of methods that exploits using deep architectures to learn high-level feature representations from data. Prerequisites: basic calculus, probability, linear algebra. Course Schedule Jan 14, 16, 21, 23 (9:20-10:50am) @ IS Building Room L2 Two video options are available: [1] Video (HD) includes slide synchronization and requires Adobe Flash Player version 10 or above. [2] Video (Youtube) may be faster to load and is recommended if you have trouble with Video (HD). If you find errors, typos, or bugs in the slides/video, please let me know. Useful References

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