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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]
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]
The semi-supervised approach OpenAI employed to make a large-scale generative system—and was first to do with a transformer model—involved two stages: an unsupervised generative "pretraining" stage to set initial parameters using a language modeling objective, and a supervised discriminative "fine-tuning" stage to adapt these parameters to ...
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 ...
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 ...
[11] [12] The model can perform tasks in robotics competitively without the need for retraining or fine-tuning. [13] In May 2023, Google announced PaLM 2 at the annual Google I/O keynote. [14] PaLM 2 is reported to be a 340 billion-parameter model trained on 3.6 trillion tokens. [15]
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.