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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.
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]
Knowledge distillation. In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [can be used] as a low-level cross-framework language to develop custom components such as layers, models ...
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 ...
blog.research.google /2020 /02 /exploring-transfer-learning-with-t5.html T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI . Introduced in 2019, [1] T5 models are trained on a massive dataset of text and code using a text-to-text framework.
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 ...
Vision transformer. A vision transformer ( ViT) is a transformer designed for computer vision. [1] A ViT breaks down an input image into a series of patches (rather than breaking up text into tokens ), serialises each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication.