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  2. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] Hyperparameter optimization determines the set of ...

  3. Hyperparameter (machine learning) - Wikipedia

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

    In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).

  4. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    In the adaptive control literature, the learning rate is commonly referred to as gain. [2] In setting a learning rate, there is a trade-off between the rate of convergence and overshooting. While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that ...

  5. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  6. Keras - Wikipedia

    en.wikipedia.org/wiki/Keras

    Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library , and later supporting more.

  7. Bayesian optimization - Wikipedia

    en.wikipedia.org/wiki/Bayesian_optimization

    Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.

  8. Hyperparameter (Bayesian statistics) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(Bayesian...

    In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution , then:

  9. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [ 1 ] [ 2 ] and training cost.