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last major update: 25 October 1996: minor update 22 April 1998 and 12 Sept 2001: links updated (they were out of date) 12 Sept 2001; fix to math font (thanks Sietse Brouwer) 2 April 2003 This document is a roughly HTML-ised version of a talk given at the NSYN meeting in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! Why would anyone want a `new' sort of computer? 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? Good at Not so good at Fast arithmetic
Khan Academy
New Pattern Found in Prime Numbers
(PhysOrg.com) -- Prime numbers have intrigued curious thinkers for centuries. On one hand, prime numbers seem to be randomly distributed among the natural numbers with no other law than that of chance. But on the other hand, the global distribution of primes reveals a remarkably smooth regularity. This combination of randomness and regularity has motivated researchers to search for patterns in the distribution of primes that may eventually shed light on their ultimate nature. In a recent study, Bartolo Luque and Lucas Lacasa of the Universidad Politécnica de Madrid in Spain have discovered a new pattern in primes that has surprisingly gone unnoticed until now. “Mathematicians have studied prime numbers for centuries,” Lacasa told PhysOrg.com. Benford’s law (BL), named after physicist Frank Benford in 1938, describes the distribution of the leading digits of the numbers in a wide variety of data sets and mathematical sequences. “BL is a specific case of GBL,” Lacasa explained.
PyBrain
<em>g</em>, a Statistical Myth
g, a Statistical Myth Attention Conservation Notice: About 11,000 words on the triviality of finding that positively correlated variables are all correlated with a linear combination of each other, and why this becomes no more profound when the variables are scores on intelligence tests. Unlikely to change the opinion of anyone who's read enough about the area to have one, but also unlikely to give enough information about the underlying statistical techniques to clarify them to novices. Includes multiple simulations, exasperation, and lots of unwarranted intellectual arrogance on my part. Follows, but is independent of, two earlier posts on the subject of intelligence and its biological basis, and their own sequel on heritability and malleability. This doubtless more than exhausts your interest in reading about the subject; it has certainly exhausted my interest in writing about it. The origin of g: Spearman's original general factor theory The modern g (And it's not just me.
Hammack Home
This book is an introduction to the standard methods of proving mathematical theorems. It has been approved by the American Institute of Mathematics' Open Textbook Initiative. Also see the Mathematical Association of America Math DL review (of the 1st edition), and the Amazon reviews. The second edition is identical to the first edition, except some mistakes have been corrected, new exercises have been added, and Chapter 13 has been extended. Order a copy from Amazon or Barnes & Noble for $13.75 or download a pdf for free here. Part I: Fundamentals Part II: How to Prove Conditional Statements Part III: More on Proof Part IV: Relations, Functions and Cardinality Thanks to readers around the world who wrote to report mistakes and typos! Instructors: Click here for my page for VCU's MATH 300, a course based on this book. I will always offer the book for free on my web page, and for the lowest possible price through on-demand publishing.
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 ! :-)...non seulement dormir mais construire aussi !
Scientific Speed Reading: How to Read 300% Faster in 20 Minutes
(Photo: Dustin Diaz) How much more could you get done if you completed all of your required reading in 1/3 or 1/5 the time? Increasing reading speed is a process of controlling fine motor movement—period. This post is a condensed overview of principles I taught to undergraduates at Princeton University in 1998 at a seminar called the “PX Project.” I have never seen the method fail. The PX Project The PX Project, a single 3-hour cognitive experiment, produced an average increase in reading speed of 386%. It was tested with speakers of five languages, and even dyslexics were conditioned to read technical material at more than 3,000 words-per-minute (wpm), or 10 pages per minute. If you understand several basic principles of the human visual system, you can eliminate inefficiencies and increase speed while improving retention. First, several definitions and distinctions specific to the reading process: You do not read in a straight line, but rather in a sequence of saccadic movements (jumps).
Brain size and evolution - complexity, "behavioral complexity", and brain size
From Serendip Organisms have indeed gotten more "complex" over evolutionary time, at least on a broad scale Organisms differ in "behavioral complexity" Organisms differ in brain sizeThere is some relation between "behavioral complexity" and brain size, but humans do not have the largest brains. There is a better relation between "behavioral complexity" and brain size in relation to body size. from Harry J. I'm still more interested in whether there is life on Mars?.
