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  2. Normalization (machine learning) - Wikipedia

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

    Weight normalization (WeightNorm) [18] is a technique inspired by BatchNorm that normalizes weight matrices in a neural network, rather than its activations. One example is spectral normalization , which divides weight matrices by their spectral norm .

  3. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    Another possible reason for the success of batch normalization is that it decouples the length and direction of the weight vectors and thus facilitates better training. By interpreting batch norm as a reparametrization of weight space, it can be shown that the length and the direction of the weights are separated and can thus be trained separately.

  4. Flow-based generative model - Wikipedia

    en.wikipedia.org/wiki/Flow-based_generative_model

    A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.

  5. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    Weight initialization [ edit ] Kumar suggested that the distribution of initial weights should vary according to activation function used and proposed to initialize the weights in networks with the logistic activation function using a Gaussian distribution with a zero mean and a standard deviation of 3.6/sqrt(N) , where N is the number of ...

  6. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...

  7. 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 ...

  8. 32-year-old woman bludgeons mother to death inside her own ...

    www.aol.com/32-old-woman-bludgeons-mother...

    A 32-year-old woman has been arrested after allegedly bludgeoning her mother to death inside her own home, officials said. Burlington County Prosecutor LaChia L. Bradshaw and Willingboro Township ...

  9. Neural network (machine learning) - Wikipedia

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

    The values of parameters are derived via learning. Examples of hyperparameters include learning rate, the number of hidden layers and batch size. [citation needed] The values of some hyperparameters can be dependent on those of other hyperparameters. For example, the size of some layers can depend on the overall number of layers. [citation needed]