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  2. COST Hata model - Wikipedia

    en.wikipedia.org/wiki/COST_Hata_model

    The COST Hata model is a radio propagation model (i.e. path loss) that extends the urban Hata model (which in turn is based on the Okumura model) to cover a more elaborated range of frequencies (up to 2 GHz). It is the most often cited of the COST 231 models (EU funded research project ca. April 1986 – April 1996), [ 1] also called the Hata ...

  3. Freepik - Wikipedia

    en.wikipedia.org/wiki/Freepik

    Freepik (stylized as FREEP!K) is an image bank website.Content produced and distributed by the online platform includes photographs, illustrations and vector images. The platform distributes its content under a freemium model, which means that users can access much of the content for free, but it is also possible to purchase a subscription with advantages such as access to more exclusive ...

  4. Hinge loss - Wikipedia

    en.wikipedia.org/wiki/Hinge_loss

    The hinge loss is a convex function, so many of the usual convex optimizers used in machine learning can work with it. It is not differentiable, but has a subgradient with respect to model parameters w of a linear SVM with score function that is given by. Plot of three variants of the hinge loss as a function of z = ty: the "ordinary" variant ...

  5. A former OpenAI researcher sees a clear path to AGI this ...

    www.aol.com/finance/former-openai-researcher...

    Aschenbrenner is a former researcher on OpenAI’s Superalignment team who was fired for allegedly “leaking information,” although he says he was fired after raising concerns to OpenAI's board ...

  6. Loss functions for classification - Wikipedia

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

    In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [ 1] Given as the space of all possible inputs ...

  7. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    The loss function is a function that maps values of one or more variables onto a real number intuitively representing some "cost" associated with those values. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a training example has propagated through the network.

  8. David meets Goliath: Japan and Korea make startups work with ...

    www.aol.com/finance/david-meets-goliath-japan...

    The second advantage of this open innovation model is that the keiretsu and chaebol get access to new ideas and products. Several Japanese and Korean policymakers told us that they were worried ...

  9. From grief to good: How maker spaces help family honor child ...

    www.aol.com/grief-good-maker-spaces-help...

    It took years for Noelle Conover, her husband and their children to see their way through grief over loss of their son Matthew, who died in 2002 from non-Hodgkins lymphoma at just 12 years old ...