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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

    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.

  4. 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 ...

  5. Dimensionless numbers in fluid mechanics - Wikipedia

    en.wikipedia.org/wiki/Dimensionless_numbers_in...

    Dimensionless numbers (or characteristic numbers) have an important role in analyzing the behavior of fluids and their flow as well as in other transport phenomena. [1] They include the Reynolds and the Mach numbers, which describe as ratios the relative magnitude of fluid and physical system characteristics, such as density, viscosity, speed of sound, and flow speed.

  6. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

  7. 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.

  8. Keira Knightley, 39, Learned 3 Different Forms of Martial ...

    www.aol.com/lifestyle/keira-knightley-39-learned...

    Keira Knightley, 39, stars in Netflix's new spy thriller 'Black Doves.' Here's how she trained for the series, plus all the details on her workouts and diet.

  9. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    The name momentum stems from an analogy to momentum in physics: the weight vector , thought of as a particle traveling through parameter space, [30] incurs acceleration from the gradient of the loss ("force"). Unlike in classical stochastic gradient descent, it tends to keep traveling in the same direction, preventing oscillations.