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  2. Completing the square - Wikipedia

    en.wikipedia.org/wiki/Completing_the_square

    Animation depicting the process of completing the square. ( Details, animated GIF version) In elementary algebra, completing the square is a technique for converting a quadratic polynomial of the form to the form for some values of h and k . In other words, completing the square places a perfect square trinomial inside of a quadratic expression.

  3. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    Regularized least squares ( RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations.

  4. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    t. e. In machine learning, backpropagation is a gradient estimation method commonly used for training neural networks to compute the network parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single ...

  5. How Ferguson elevated the profile of the Justice Department's ...

    www.aol.com/news/ferguson-elevated-profile...

    As the first images out of Ferguson, Missouri surfaced 10 years ago — the bloodied body of a man left for hours in the street beneath white sheets, protesters smashing car windows and looting ...

  6. Voter registration scams are now everywhere. Here's how to ...

    www.aol.com/news/voter-registration-scams-now...

    In Shasta County, California, the county clerk and election officials warned last week that a text message asking recipients to click a link to register to vote was a scam. Officials said clicking ...

  7. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    e. Bayes consistent loss functions: Zero-one loss (gray), Savage loss (green), Logistic loss (orange), Exponential loss (purple), Tangent loss (brown), Square loss (blue) Proposed since April 2024. In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the ...

  8. Sum-of-squares optimization - Wikipedia

    en.wikipedia.org/wiki/Sum-of-Squares_Optimization

    Sum-of-squares optimization. A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variables. These constraints are of the form that when the decision variables are used as coefficients in certain polynomials, those polynomials should have the polynomial ...

  9. Lanchester's laws - Wikipedia

    en.wikipedia.org/wiki/Lanchester's_laws

    Lanchester's square law calculates the number of soldiers lost on each side using the following pair of equations. [7] Here, dA/dt represents the rate at which the number of Red soldiers is changing at a particular instant. A negative value indicates the loss of soldiers. Similarly, dB/dt represents the rate of change of the number of Blue ...