Search results
Results from the WOW.Com Content Network
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Continuous monitoring helps identify when the model needs retraining or recalibration. For pre-trained models, periodic fine-tuning may suffice to keep the model performing optimally, while models built from scratch may require more extensive updates depending on how the system was designed. [43] [44]
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.
Retraining or refresher training is the process of learning a new or the same old skill or trade for the same group of personnel. Retraining is required [by whom?] to be provided on a regular basis to avoid personnel obsolescence due to technological changes and the individuals' memory capacity. This short-term instruction course shall serve to ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
For premium support please call: 800-290-4726 more ways to reach us
Fine-tuning may refer to: Fine-tuning (deep learning) Fine-tuning (physics) Fine-tuned universe; See also. Tuning (disambiguation) This page was last edited on 24 ...
News. Science & Tech