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  2. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.

  3. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    Instance normalization (InstanceNorm), or contrast normalization, is a technique first developed for neural style transfer, and is also only used for CNNs. [26] It can be understood as the LayerNorm for CNN applied once per channel, or equivalently, as group normalization where each group consists of a single channel:

  4. Transaction processing system - Wikipedia

    en.wikipedia.org/wiki/Transaction_processing_system

    The most recent backup is the son, the previous the father, and the oldest backup is the grandfather. This method is commonly used for a batch transaction processing system with a magnetic tape drive. If the system fails during a batch run, the master file is recreated by restoring the son backup and then restarting the batch.

  5. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Without normalization, the clusters were arranged along the x-axis, since it is the axis with most of variation. After normalization, the clusters are recovered as expected. In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature ...

  6. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.

  7. Normalization (statistics) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(statistics)

    In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment.

  8. Amazon Flex drivers are using bots to cheat their way to ...

    www.aol.com/amazon-flex-drivers-using-bots...

    Once scripts became too expensive, he began using a third-party app called Flex Utility, which costs about $20 to download from the Google Play Store. ... As Amazon has cracked down on bots, some ...

  9. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    One can normalize input scores by assuming that the sum is zero (subtract the average: where =), and then the softmax takes the hyperplane of points that sum to zero, =, to the open simplex of positive values that sum to 1 =, analogously to how the exponent takes 0 to 1, = and is positive.

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