"during training process" is a grammatically correct phrase and can be used in written English.
It is commonly used to describe a specific period of time when someone is undergoing training or participating in a training process. Example: "During the training process, employees are taught the necessary skills and knowledge to excel in their roles."
The performance was monitored during training process as sum squared error over all the training data.
Weight and biases' values will be assigned progressively and corrected during training process comparing the predicted outputs with known outputs.
53 90 53 37 R2 0.77 0.72 0.76 0.59 MAE 0.365 0.496 0.377 0.667 a1.0 and 0.1 weight values were used for steroid and azaheterocycle data, respectively, during training process bNot applicable Fig. 2 Scatter plots of pIC50 calculated with steroid (a, b) and generalized (c, d) 3D-QSAR models for the training sets (a, c) and external validation sets (b, d).
It has been demonstrated that a sense of self-confidence and decision-making process develops in a person receiving the simulation training because of the tolerance to errors during training process unlike real-life [ 15, 16].
During training process, the jackknife operations were conducted on such 14 datasets to optimize the SVM parameters using the search function SVMcgForClass, which was downloaded from http://www.matlabsky.com/.com/
The availability of an 'expert' in anthropometry may be crucial not only during the training process but also during data collection supervision.
Various architectures were tried during the training process.
The remaining parameters for the ConvNet classifier were tuned during the training process.
In sequential learning, during the training process no information is re-presented to the network.
Robust estimation of the diffusion coefficients, however, requires introducing certain levels of stochastic variations during the training process.
The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity.
