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    sasan Posted 2015-04-20 15:05:58Z
    OPEN DATA PLATFORM [Clicked : 1]
    Enabling Big Data solutions to flourish atop a common core platform.
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    sasan Posted 2015-04-15 09:39:42Z
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    sasan Posted 2015-04-14 04:33:31Z
    The following table lists Microsoft SQL Server data types, their equivalents in the common language runtime (CLR) for SQL Server in the System.Data.SqlTypes namespace, and their native CLR equivalents in the Microsoft .NET Framework.

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    sasan Posted 2015-04-14 03:12:21Z
    In this article we are going to see how to create a rounded corners form in C#, So far that we are going to create a custom form, we can do it in many ways by overriding the Paint method and write customized code, otherwise used the extern method.
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    sasan Posted 2015-04-14 02:59:24Z
    AngularJS Article [Clicked : 0]
    AngularJS is an extensible and exciting new JavaScript MVC framework developed by Google for building well-designed, structured and interactive single-page applications (SPA). It lays strong emphasis on Testing and Development best practices such as templating and declarative bi-directional data binding. 
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    sasan Posted 2015-04-09 04:21:45Z
    What's New in C# 6 [Clicked : 1]
    Are you a C# Developer who wants to be more productive? We've got good news! The latest and greatest features in C# can help. Join us to learn a more precise way to write constructs (that is, find out how to type less and code more).

    MVP and best-selling author Bill Wagner teams up with Microsoft Program Manager Anthony Green to explore auto property initializes, expression bodied members, null propagation operators, exception filters, string interpolation, and more. Find out how these new language features can make your C# development more efficient. And see how easy it is to look at your code, diagnose issues, and solve problems.
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    Reza Posted 2015-04-07 20:25:37Z
    STEPHEN WALTHER:
    I spent the last couple of weeks writing sample code for ASP.NET 5/MVC 6 and I was surprised by the depth of the changes in the current beta release of ASP.NET 5. ASP.NET 5 is the most significant new release of ASP.NET in the history of the ASP.NET framework — it has been rewritten from the ground up.In this blog post, I list what I consider to be the top 10 most significant changes in ASP.NET 5. This is a highly opinionated list. If other changes strike you as more significant, please describe the change in a comment.
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    moeinkiller2005 Posted 2015-04-03 08:52:31Z
    What is Resampling?

    Resampling is a method which researchers use to determine where their model is accurate enough or not and also find different problem of their model. The common process in machine learning is taking a part of all data and use it as a validation set, the method which is called Cross-Validation resampling.

    1-Randomization exact test:
    Randomization exact test is a test procedure in which data arerandomly re-assigned so that an exact p-value is calculated based on the permutateddata.

    2-Cross validation
    Simple cross-validation. Take regression as an example. In the process of implementinga simple cross-validation, the first sub-sample is usually used for deriving the regressionequation while another sub-sample is used for generating predicted scores from the firstregression equation. Next, the cross-validity coefficient is computed by correlating thepredicted scores and the observed scores on the outcome variable.

    Double cross-validation. Double cross-validation is a step further than its simplecounterpart. Take regression as an example again. In double cross-validation regressionequations are generated in both sub-samples, and then both equations are used togenerate predicted scores and cross-validity coefficients.

    Multicross-validation. Multicross-validation is an extension of double cross-validation.In this form of cross-validation, double cross-validation procedures are repeated manytimes by randomly selecting sub-samples from the data set. In the context of regressionanalysis, beta weights computed in each sub-sample are used to predict the outcomevariable in the corresponding sub-sample. Next, the observed and predicted scores of theoutcome variable in each sub-sample are used to compute the cross validated coefficient.

    3-Jackknife
    Jackknife is a step beyond cross-validation. In Jackknife, the same test is repeated byleaving one subject out each time. Thus, this technique is also called leave one out. Thisprocedure is especially useful when the dispersion of the distribution is wide or extremescores are present in the data set. In these cases it is expected that Jackknife couldreturn a bias-reduced estimation.

    4-Bootstrap
    in bootstrap, the originalsample could be duplicated as many times as the computing resources allow, and thenthis expanded sample is treated as a virtual population. Then samples are drawn fromthis population to verify the estimators. Obviously the "source" for resampling inbootstrap could be much larger than that in the other two. In addition, unlike crossvalidation and Jackknife, the bootstrap employs sampling with replacement
      Update History
      Updated 2015-04-09 07:08:04Z
      What is Resampling?

      Resampling is a method which researchers use to determine where their model is accurate enough or not and also find different problem of their model. The common process in machine learning is taking a part of all data and use it as a validation set, the method which is called Cross-Validation resampling.

      1-Randomization exact test:
      Randomization exact test is a test procedure in which data arerandomly re-assigned so that an exact p-value is calculated based on the permutateddata.

      2-Cross validation
      Simple cross-validation. Take regression as an example. In the process of implementinga simple cross-validation, the first sub-sample is usually used for deriving the regressionequation while another sub-sample is used for generating predicted scores from the firstregression equation. Next, the cross-validity coefficient is computed by correlating thepredicted scores and the observed scores on the outcome variable.

      Double cross-validation. Double cross-validation is a step further than its simplecounterpart. Take regression as an example again. In double cross-validation regressionequations are generated in both sub-samples, and then both equations are used togenerate predicted scores and cross-validity coefficients.

      Multicross-validation. Multicross-validation is an extension of double cross-validation.In this form of cross-validation, double cross-validation procedures are repeated manytimes by randomly selecting sub-samples from the data set. In the context of regressionanalysis, beta weights computed in each sub-sample are used to predict the outcomevariable in the corresponding sub-sample. Next, the observed and predicted scores of theoutcome variable in each sub-sample are used to compute the cross validated coefficient.

      Jackknife
      Jackknife is a step beyond cross-validation. In Jackknife, the same test is repeated byleaving one subject out each time. Thus, this technique is also called leave one out. Thisprocedure is especially useful when the dispersion of the distribution is wide or extremescores are present in the data set. In these cases it is expected that Jackknife couldreturn a bias-reduced estimation.

      Bootstrap
      in bootstrap, the originalsample could be duplicated as many times as the computing resources allow, and thenthis expanded sample is treated as a virtual population. Then samples are drawn fromthis population to verify the estimators. Obviously the "source" for resampling inbootstrap could be much larger than that in the other two. In addition, unlike crossvalidation and Jackknife, the bootstrap employs sampling with replacement
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    moeinkiller2005 Posted 2015-03-18 07:26:01Z

    Traditional Overfitting
    Parameter Tweak Overfitting
    Resampling Bias and Variance
    Bad Statistics
    Information Leakage
    Human-loop overfitting
    Non-Stationary Distributions
    Sampling
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    moeinkiller2005 Posted 2015-03-17 06:21:14Z
    A great list of Machine Learning topics made by Startup.ML

    Deep Learning
    Online Learning
    Graphical Models
    Structured Predictions
    Ensemble Methods
    Kernel Machines
    Hyper-Parameter Optimization
    OptimizationGraphs
    Hadoop Spark
    GPU Learning
    JuliaRobots
    Natural Language Processing
    Visualization
    Computer Vision
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