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12 IT skills that employers can't say no to. Have you spoken with a high-tech recruiter or professor of computer science lately?

12 IT skills that employers can't say no to

According to observers across the country, the technology skills shortage that pundits were talking about a year ago is real (see "Workforce crisis: Preparing for the coming IT crunch"). "Everything I see in Silicon Valley is completely contrary to the assumption that programmers are a dying breed and being offshored," says Kevin Scott, senior engineering manager at Google Inc. and a founding member of the professions and education boards at the Association for Computing Machinery.

"From big companies to start-ups, companies are hiring as aggressively as possible. " Also check out our updated 8 Hottest Skills for '08. Machine Learning. Machine learning is the science of getting computers to act without being explicitly programmed.

Machine Learning

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine Learning (Stanford) Euclidean space. This article is about Euclidean spaces of all dimensions.

Euclidean space

For 3-dimensional Euclidean space, see 3-dimensional space. A sphere, the most perfect spatial shape according to Pythagoreans, also is an important concept in modern understanding of Euclidean spaces Every point in three-dimensional Euclidean space is determined by three coordinates. Intuitive overview[edit] In order to make all of this mathematically precise, the theory must clearly define the notions of distance, angle, translation, and rotation for a mathematically described space. Hilbert space. The state of a vibrating string can be modeled as a point in a Hilbert space.

Hilbert space

The decomposition of a vibrating string into its vibrations in distinct overtones is given by the projection of the point onto the coordinate axes in the space. Hilbert spaces arise naturally and frequently in mathematics and physics, typically as infinite-dimensional function spaces. MATLAB - The Language of Technical Computing. Octave. Executable versions of GNU Octave for GNU/Linux systems are provided by the individual distributions.


Distributions known to package Octave include Debian, Ubuntu, Fedora, Gentoo, and openSUSE. These packages are created by volunteers. The delay between an Octave source release and the availability of a package for a particular GNU/Linux distribution varies. The Octave Wiki has instructions for installing Octave on OS X systems. Octave may also be available in third-party package managers such as Homebrew, Macports, or Fink.

Executable versions of Octave for BSD systems are provided by the individual distributions. Windows binaries with corresponding source code can be downloaded from The latest released version of Octave is always available from. Big O notation. Example of Big O notation: f(x) ∈ O(g(x)) as there exists c > 0 (e.g., c = 1) and x0 (e.g., x0 = 5) such that f(x) < cg(x) whenever x > x0.

Big O notation

Big O notation characterizes functions according to their growth rates: different functions with the same growth rate may be represented using the same O notation. The letter O is used because the growth rate of a function is also referred to as order of the function. A description of a function in terms of big O notation usually only provides an upper bound on the growth rate of the function. Associated with big O notation are several related notations, using the symbols o, Ω, ω, and Θ, to describe other kinds of bounds on asymptotic growth rates. Big O notation is also used in many other fields to provide similar estimates. Formal definition[edit] Frequentism and Bayesianism IV: How to be a Bayesian in Python.

Introduction to Bayesian Methods. Round-up of Web Browser Internals Resources - HTML5Rocks Updates. In many cases, we treat web browsers as a black box.

Round-up of Web Browser Internals Resources - HTML5Rocks Updates

But as we gain a better understanding of how they work, we not only recognize where to make smart optimizations but also we push them farther. The links below capture most of the resources that explain the innerworkings of web browsers. <img src=" class=big> How Browsers Work: Behind the scenes of modern web browsers, by Tali Garsiel How Browsers Work – Architecture, by Vineet Gupta Know Your JavaScript Engines, by David Mandelin From Console to Chrome, by Lilli Thompson <img src=" class=big> Fast CSS: How Browsers Lay Out Web Pages, by David Baron What Browsers Really Think of your App, by Alex Russell Faster HTML and CSS: Layout Eng­ine Internals for Web Dev­elop­ers, by David Baron CSS Selectors parsed right to left. Why? Getting Started - Git « Some thoughts, ideas and fun!!! Overview.

Getting Started - Git « Some thoughts, ideas and fun!!!

Ross's Blog » Blog Archive » Toolbox for learning machine learning and data science. Posted: September 6th, 2012 | Author: admin | Filed under: Uncategorized | 9 Comments »

Ross's Blog » Blog Archive » Toolbox for learning machine learning and data science

Bayes' Theorem Illustrated (My Way) - Less Wrong. (This post is elementary: it introduces a simple method of visualizing Bayesian calculations.

Bayes' Theorem Illustrated (My Way) - Less Wrong

In my defense, we've had other elementary posts before, and they've been found useful; plus, I'd really like this to be online somewhere, and it might as well be here.) I'll admit, those Monty-Hall-type problems invariably trip me up. Download. 1 Choose the nightly development build of H2O to get the very latest tools, including features that are still in development. 2 Choose the latest stable release to use a version of H2O that offers cutting edge analytics, and has been tested and documented. Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.

ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Machine learning. Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs.

Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Overview[edit] Tom M. Yes, Computers Can Think. NEW HAVEN— Last year, after Garry Kasparov's chess victory over the I.B.M. computer Deep Blue, I told the students in my Introduction to Artificial Intelligence class that it would be many years before computers could challenge the best humans. Now that I and many others have been proved wrong, a lot of people have been rushing to assure us that Deep Blue is not actually intelligent and that this victory has no bearing on the future of artificial intelligence.

Although I agree that the computer is not very intelligent, to say that it shows no intelligence at all demonstrates a basic misunderstanding of what it does and of the goals and methods of artificial intelligence research. True, Deep Blue is very narrow. It can win a chess game, but it can't recognize, much less pick up, a chess piece. Deep Blue (chess computer) Deep Blue After Deep Thought's 1989 match against Kasparov, IBM held a contest to rename the chess machine and it became "Deep Blue", a play on IBM's nickname, "Big Blue".[8] After a scaled down version of Deep Blue, Deep Blue Jr., played Grandmaster Joel Benjamin, Hsu and Campbell decided that Benjamin was the expert they were looking for to develop Deep Blue's opening book, and Benjamin was signed by IBM Research to assist with the preparations for Deep Blue's matches against Garry Kasparov.[9] On February 10, 1996, Deep Blue became the first machine to win a chess game against a reigning world champion (Garry Kasparov) under regular time controls.

Watson (computer) Watson is an artificially intelligent computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson.[3][4] The computer system was specifically developed to answer questions on the quiz show Jeopardy! Erik Brynjolfsson: The key to growth? Race with the machines. Jeremy Howard: The wonderful and terrifying implications of computers that can learn. Alison Gopnik. Alison Gopnik takes us into the fascinating minds of babies and children, and shows us how much we understand before we even realize we do. Why you should listen What’s it really like to see through the eyes of a child? Are babies and young children just empty, irrational vessels to be formed into little adults, until they become the perfect images of ourselves?

On the contrary, argues Alison Gopnik, professor of psychology and philosophy at the University of California at Berkeley. The author of The Philosophical Baby, The Scientist in the Crib and other influential books on cognitive development, Gopnik presents evidence that babies and children are conscious of far more than we give them credit for, as they engage every sense and spend every waking moment discovering, filing away, analyzing and acting on information about how the world works. Meet the startups making machine learning an elementary affair. Jeff Hawkins: How brain science will change computing.

Redwood Center for Theoretical Neuroscience.