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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]
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).
While other RL algorithms require hyperparameter tuning, PPO comparatively does not require as much (0.2 for epsilon can be used in most cases). [15] Also, PPO does not require sophisticated optimization techniques. It can be easily practiced with standard deep learning frameworks and generalized to a broad range of tasks.
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.
The learning rate and its adjustments may also differ per parameter, in which case it is a diagonal matrix that can be interpreted as an approximation to the inverse of the Hessian matrix in Newton's method. [5] The learning rate is related to the step length determined by inexact line search in quasi-Newton methods and related optimization ...
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]
AdaGrad (for adaptive gradient algorithm) is a modified stochastic gradient descent algorithm with per-parameter learning rate, first published in 2011. [38] Informally, this increases the learning rate for sparser parameters [clarification needed] and decreases the learning rate for ones that are less sparse. This strategy often improves ...
Model selection - choosing which machine learning algorithm to use, often including multiple competing software implementations; Ensembling - a form of consensus where using multiple models often gives better results than any single model [6] Hyperparameter optimization of the learning algorithm and featurization Neural architecture search