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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]
The name is an acronym for the five phases it defines for building training and performance support tools: Analysis; Design; Development; Implementation; Evaluation; Most current ISD models are variations of the ADDIE process. [2] Other models include the Dick and Carey and Kemp ISD models. Rapid prototyping is another common alternative.
Instructional design (ID), also known as instructional systems design and originally known as instructional systems development (ISD), is the practice of systematically designing, developing and delivering instructional materials and experiences, both digital and physical, in a consistent and reliable fashion toward an efficient, effective, appealing, engaging and inspiring acquisition of ...
For the special case where (,) = (,) is a joint probability distribution and the loss function is the negative log likelihood (,), a risk minimization algorithm is said to perform generative training, because can be regarded as a generative model that explains how the data were generated. Generative training algorithms are often simpler and ...
Baldwin and Ford (1988) [6] is the most commonly cited model of transfer, which defines the transfer of training as the generalization and maintenance of material learned in training to the work environment. [7] Within this model, the authors conceptualize transfer of training as a three-stage process.
The model was used at Gordon Training International by its employee Noel Burch in the 1970s; there it was called the "four stages for learning any new skill". [5] Later the model was frequently attributed to Abraham Maslow, incorrectly since the model does not appear in his major works. [6]
In transfer learning, a model designed for one task is reused on a different task. [13] Training an autoencoder intrinsically constitutes a self-supervised process, because the output pattern needs to become an optimal reconstruction of the input pattern itself. However, in current jargon, the term 'self-supervised' often refers to tasks based ...
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).