training data

training data

[′trān·iŋ ‚dad·ə]
(control systems)
Data entered into a robot's computer at the beginning of an operation.
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But acquiring training data for each domain in each supported language is cumbersome and costly, which has become more of a problem as companies demand intelligent, conversational voice apps to support global product strategies.
numerous recent convective-scale applications of the met offices unified model in southeast asia, combined with data from a mature global ensemble (mogreps-g) provide a wealth of training data to assess the nature of tropical forecast errors.
For enterprise learning systems, deep or otherwise, the pervasive influence of human bias reflected in the selection (curation) of training data is a critical factor in both the success of the system in achieving its objectives and in the understanding on the part of the human designer of how the system is doing what it does.
This ability could help make computers smarter by sidestepping the need to feed them painstakingly labeled training data.
The main aim of using ranking technique is to produce of a permutation of data items, in unseen list which is similar to training data set.
In the deep learning process, it is necessary to do massive calculations based on training data, but the upper limit to processing performance is determined by the volume of electricity that can be used by the servers and other hardware that carry out the learning processing, so increasing performance per watt has become an issue in accelerating deep learning processing.
The company said the capital will be used to fuel the adoption of CrowdFlower AI that combines training data, machine learning and human-in-the-loop in a single platform.
The use of divide-and conquer system modeling combined with the LPV ROM will provide a good approximation to the thermal behavior of the battery module while significantly reducing the time required to generate the training data.
The process of training a NN model is to assemble the neurons following a certain structure, and to predict the associated weights based on the training data.
We applied active learning, random under sampling and adjacent class merging to improve the quality of training data.
Because context-dependent models contain more parameters than context-independent models, they require more training data for accurate parameter estimation.

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