The phrase "quantity training" is grammatically correct and is commonly used in written English.
It refers to a type of training that focuses on increasing the amount or volume of something, such as production or output. Example: Our company is implementing a new quantity training program for our employees in order to increase our productivity and meet the growing demand for our products.
To further reduce overfitting and improve the performance with a small quantity of training samples, we firstly increase the training dataset by horizontal reflection and then learn MLeNet via combining both pre-training and fine-tuning.
The bulk of the debate in terms of quantity of training has focussed on the reduction in hours of training following the widely-opposed introduction of the European Working Time Directive [ 14, 15], and more recently the length of training[ 2].
Along with that, Data Augmentation (DA), which is a common strategy adopted to increase the quantity of training data, has been shown to be effective for neural network training to make invariant predictions.
Consensus theory is employed for fusion of results provided by different sources The results show with only small quantity of training data provided and the testing data being much more different from training data, the performances of deterministic or stochastic faults diagnosis is better than or equally well as multivariate statistics based approaches studied in [17].
Thus the focus has shifted from not only the quantity of training that is provided but also the quality and appropriateness of that training [ 2].
That includes paying close attention to quality and quantity of training and educating Afghan security forces, supplying them with modern weapons and vehicles, and rehabilitating the Afghan air force, he said.
In this study, DOE⿿CCRD full factorial design was incorporated into the ANN methodology, so the need of a large quantity of training data was avoided.
Anyone with experience in the artificial intelligence space will tell you that quality and quantity of training data is one of the most important inputs in building real-world-functional AI.
While information on individuals' motivation for completing this quantity of training was not available, it appears to be in line with state child care licensing requirements, as well.
This demonstrates that it is not safe to assume that performance problems should immediately be attributed to the quantity of training data available.
