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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...

    In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization and activation normalization . Data normalization (or feature scaling ) includes methods that rescale input data so that the features have the same range, mean, variance, or other ...

  4. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    In machine learning, ... Batch normalization is a standard method for solving both the exploding and the vanishing gradient problems. [10] [11] Multi-level hierarchy

  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. Neural network Gaussian process - Wikipedia

    en.wikipedia.org/wiki/Neural_network_Gaussian...

    Deep learning and artificial neural networks are approaches used in machine learning ... skip connection, attention, batch normalization, and/or layer normalization. ...

  7. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    Batch learning; Curriculum learning ... The normalization ensures that the sum of the components of ... though the term "softmax" is conventional in machine learning ...

  8. Inception (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Inception_(deep_learning...

    Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.

  9. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data.Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data.