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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).
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] [3]
In machine learning, the term "softmax" is credited to John S. Bridle in two 1989 conference papers, Bridle (1990a): [16]: 1 and Bridle (1990b): [3] We are concerned with feed-forward non-linear networks (multi-layer perceptrons, or MLPs) with multiple outputs.
Hyperparameter (machine learning) Hyperparameter (Bayesian statistics) This page was last edited on 5 ...
A sample subset containing minimal number of data items is randomly selected from the input dataset. A fitting model with model parameters is computed using only the elements of this sample subset. The cardinality of the sample subset (e.g., the amount of data in this subset) is sufficient to determine the model parameters.
SINGA-Auto (aka. Rafiki [5] in VLDB2018) is a subsystem of Apache SINGA to provide the training and inference service of machine learning models. SINGA-Auto frees users from constructing the machine learning models, tuning the hyper-parameters, and optimizing the prediction accuracy and speed. Users can simply upload their datasets, configure ...
Aside from their empirical performance, activation functions also have different mathematical properties: Nonlinear When the activation function is non-linear, then a two-layer neural network can be proven to be a universal function approximator. [6]
Instead of modifying existing methods, some add-on techniques are there, which can be directly placed on top of existing approaches to detect smaller objects. One such technique is Slicing Aided Hyper Inference(SAHI). [25] The image is sliced into different-sized multiple overlapping patches. Hyper-parameters define their dimensions. Then ...