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Illustration of transfer learning. Transfer learning ( TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Transfer of learning. Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. Transfer is not a discrete activity, but is rather an integral part of the learning process. Researchers attempt to identify when and how transfer occurs and to offer strategies to improve ...
Fine-tuning (deep learning) In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained 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" (not updated during ...
A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]
Multi-task learning. Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models ...
In organizational theory, knowledge transfer is the practical problem of transferring knowledge from one part of the organization to another. Like knowledge management, knowledge transfer seeks to organize, create, capture or distribute knowledge and ensure its availability for future users.
Distinction between usual machine learning setting and transfer learning, and positioning of domain adaptation. Domain adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning a model from a source data distribution and applying that model on a different (but related) target data ...
BERT (language model) Bidirectional Encoder Representations from Transformers ( BERT) is a language model based on the transformer architecture, notable for its dramatic improvement over previous state of the art models. It was introduced in October 2018 by researchers at Google. [1] [2] A 2020 literature survey concluded that "in a little over ...