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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]
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 ...
Thesaurus Linguae Latinae. A modern english thesaurus. A thesaurus (pl.: thesauri or thesauruses), sometimes called a synonym dictionary or dictionary of synonyms, is a reference work which arranges words by their meanings (or in simpler terms, a book where one can find different words with similar meanings to other words), [1] [2] sometimes as a hierarchy of broader and narrower terms ...
An example of a fine-tuning problem considered by the scientific community to have a plausible "natural" solution is the cosmological flatness problem, which is solved if inflationary theory is correct: inflation forces the universe to become very flat, answering the question of why the universe is today observed to be flat to such a high degree.
fine The end, often in phrases like al fine (to the end) fioritura the florid embellishment of melodic lines, either notated by a composer or improvised during a performance. flat A symbol (♭) that lowers the pitch of a note by a semitone. Also an adjective to describe a singer or musician performing a note in which the intonation is an ...
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
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.
Power Thesaurus was created by Alexander Radyushin in 2012 and developed by Radyushin & Co. [3] [4] The domain name for the thesaurus was registered on September 18, 2012. In 2015, the android and iOS app versions of the thesaurus were developed while its Chrome and Opera browser extensions were released in 2016. [5] [6]