12 IT skills that employers can't say no to. Have you spoken with a high-tech recruiter or professor of computer science lately? 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. Many recruiters say there are more open positions than they can fill, and according to Kate Kaiser, associate professor of IT at Marquette University in Milwaukee, students are getting snapped up before they graduate. (See also "The top 10 dead (or dying) computer skills".) 1) Machine learning. Machine Learning. Machine learning is the science of getting computers to act without being explicitly programmed. 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 is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. Lecture 1 | Machine Learning (Stanford) Euclidean space. This article is about Euclidean spaces of all dimensions. 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. Once the Euclidean plane has been described in this language, it is actually a simple matter to extend its concept to arbitrary dimensions. Euclidean structure[edit] These are distances between points and the angles between lines or vectors, which satisfy certain conditions (see below), which makes a set of points a Euclidean space.
Where xi and yi are ith coordinates of vectors x and y respectively. Distance[edit] Angle[edit] (explain the notation), Hilbert space. The state of a vibrating string can be modeled as a point in a 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.
The earliest Hilbert spaces were studied from this point of view in the first decade of the 20th century by David Hilbert, Erhard Schmidt, and Frigyes Riesz. They are indispensable tools in the theories of partial differential equations, quantum mechanics, Fourier analysis (which includes applications to signal processing and heat transfer)—and ergodic theory, which forms the mathematical underpinning of thermodynamics. Definition and illustration[edit] Motivating example: Euclidean space[edit] The dot product satisfies the properties: Definition[edit] The inner product of an element with itself is positive definite: converges. MATLAB - The Language of Technical Computing. Octave. 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 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] Let f and g be two functions defined on some subset of the real numbers. If and only if there exist positive numbers δ and M such that if and only if Example[edit] For example, let so Usage[edit] or and . . . . Frequentism and Bayesianism IV: How to be a Bayesian in Python. This post is part of a 4-part series: Part I Part II Part III Part IV See also Frequentism and Bayesianism: A Python-driven Primer, a peer-reviewed article partially based on this content. I've been spending a lot of time recently writing about frequentism and Bayesianism. Here I want to back away from the philosophical debate and go back to more practical issues: in particular, demonstrating how you can apply these Bayesian ideas in Python. The workhorse of modern Bayesianism is the Markov Chain Monte Carlo (MCMC), a class of algorithms used to efficiently sample posterior distributions. Below I'll explore three mature Python packages for performing Bayesian analysis via MCMC: emcee: the MCMC Hammerpymc: Bayesian Statistical Modeling in Pythonpystan: The Python Interface to Stan I won't be so much concerned with speed benchmarks between the three, as much as a comparison of their respective APIs.
Test Problem: Line of Best Fit¶ Let's define some data that we'll work with: In [1]: In [2]: Introduction to Bayesian Methods. Round-up of Web Browser Internals Resources - HTML5Rocks Updates. In many cases, we treat web browsers as a black box. 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="
Getting Started - Git « Some thoughts, ideas and fun!!! Overview In this article I’ll be covering some the things you may need to know about Git. I’ll try and go into the concepts that Git puts out there as well as how to start using it successfully. This article will be written in such a way that it flows into my next article which will cover Git-Flow for branching strategies.
I hope you’ll enjoy both of these and that you’ll find this helpful in your own projects. What I’ll be going through are as follows: Git – What is it? Just remember that this article will be changed and things will be added and refined as I learn new things or as I find new things that are relevant for everyone to know about Git. Git – What is it? To quote the GREAT Wikipedia; “Git is a distributed revision control system with an emphasis on speed”. In this case we have a centralized Version Control Server to which all developers connect when they want to start working on a specific project. To complete this section of “Git – What is it?” Git Setup sudo apt-get install git. Ross's Blog » Blog Archive » Toolbox for learning machine learning and data science. Posted: September 6th, 2012 | Author: admin | Filed under: Uncategorized | 9 Comments » Recently I jumped in and taught myself how to do medium-sized data exploration and machine learning. (Excel-sized < My Data Set < Big Data) If you are a real data scientist or expert, skip this.
It isn’t for you. Matlab vs. If you work at a university or big company, maybe you have access to Matlab, which is apparently great, but expensive. A physicist I was working with knew and used R. Python, on the other hand, is a dream. The next step is picking the packages to support Python. Python Packages for Analysis I’m sure there are a lot of different choices with pluses and minuses, but this set served me very well, came after reasonable research, and never let me down. While I’m at it, here are some good documentation sources I used: Also, if you need to clean up your data to get it into a usable state, you might try Data Wrangler or Google Refine. Happy data exploring! (Updates) Bayes' Theorem Illustrated (My Way) - Less Wrong. (This post is elementary: it introduces a simple method of visualizing Bayesian calculations.
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. Or at least, they do if I'm not thinking very carefully -- doing quite a bit more work than other people seem to have to do. What's more, people's explanations of how to get the right answer have almost never been satisfactory to me. Minds work differently, illusion of transparency, and all that.
Fortunately, I eventually managed to identify the source of the problem, and I came up a way of thinking about -- visualizing -- such problems that suits my own intuition. I've mentioned before that I like to think in very abstract terms. ...well, let's just say I prefer to start at the top and work downward, as a general rule. Like this: Figure 0 Figure 1 Figure 2 Figure 3 and. 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. Sparkling Water — H2O’s Integration into Spark 3 For instructions on running either the zipped file or the Sandbox, read the Sparkling Water Tutorials. What is H2O? H2O makes Hadoop do math! Data collection is easy. What does H2O do? Ad hoc exploration of big data Slice big data to test and train, verify assumptions in data. Modeling Engine with high-powered math algorithms GLM/GLMnetRandom ForestGBMDeep LearningK-MeansPCA Real-time Scoring Ensembles100s of Models100s of NanosecondsEmbeddable online and offline scoring What is the interface?
REST-API and JSON allows connecting via MS Excel, google-style search bar and integrated R environment for Data Analysis. H2O brings database-like interactiveness to Hadoop. Why H2O? Need help? Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version on YouTube & iTunes 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. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? Machine learning. Study of algorithms that improve automatically through experience ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.[3][4] The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimization (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning.[6][7] From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning.
By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. Tom M. Modern-day machine learning has two objectives. [edit] Statistical physics. 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. It can't even carry on a conversation about the game it just won.
So what shall we say about Deep Blue? How about: It's a ''little bit'' intelligent. Drawing (Steven Salerno) 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.
However, Kasparov won three and drew two of the following five games, beating Deep Blue by a score of 4–2 (wins count 1 point, draws count ½ point). The match concluded on February 17, 1996. In 2003 a documentary film was made that explored these claims. Notes Jump up ^ Saletan, William (2007-05-11). Bibliography. Watson (computer) Erik Brynjolfsson: The key to growth? Race with the machines. Jeremy Howard: The wonderful and terrifying implications of computers that can learn. Alison Gopnik | Speaker. Meet the startups making machine learning an elementary affair. Jeff Hawkins: How brain science will change computing. Redwood Center for Theoretical Neuroscience.