Fine-tune Payment Format

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Steve T
2016-06-19
I am new to this program the search engine next to the description of the for would be great if you could see a picture of the for rather than having to click on it if it is not the one you want then back to the search engine
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2016-09-06
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fine-tune the process for industry by investigating how specific properties affect performance. Fine-tune the images yourself using the easy control panel. Fine-tune the product. Fine-tune the policy, the danger is that the most valuable provision risks being squeezed out.
: to make small changes to (something) in order to improve the way it works or to make it exactly right. See the full definition for fine-tune in the English Language Learners Dictionary. More from Merriam-Webster on fine-tune.
Synonyms: down, graduate, calibrate, refine, pull off, tweak, polish, pluck, pick off. Calibrate, graduate, fine-tune(verb) make fine adjustments or divide into marked intervals for optimal measuring.
Hyphenation of fine-tune Unfortunately it cannot be hyphenated because it only contains one syllable. The search term entered was not found in our database and might not be a valid English word.
The term fine-tuning is used to characterize sensitive dependence of facts or properties on the values of certain parameters. Technological devices are paradigmatic examples of fine-tuning.
Fine-tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.
Tuning is the process of maximizing a model's performance without overfitting or creating too high of a variance. In machine learning, this is accomplished by selecting appropriate hyperparameters. Hyperparameters can be thought of as the dials or knobs of a machine learning model.
Fine-tuning is one approach to transfer learning. In Transfer Learning or Domain Adaptation we train the model with a dataset, and after we train the same model with another dataset that has a different distribution of classes, or even with other classes than in the training dataset).
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