… A model's generalization ability is typically estimated during training using a separate data
set, the validation set, and used … It was created using AutoML, a Google Brain's reinforcement …

… training process. We combined these data with clinical variables to derive iGray, an individualised
radiation dose that results in an estimation … multivariable regression model with Deep …

… in terms of a relationship between an output variable \(y\) (eg, … problem, while a regression
problem refers to the situation … it to the training error we should obtain a better PE estimate. …

… Deep RNN were implemented to estimate demand at two … a suitable choice for estimating
underlying data relationship. A … of ∊ ) related to the regression models used in this study. These …

… connected layer, and one regression layer. Each convolution … to their well-known relative
signal intensity relationship (tNAA … the metabolites estimated over all simulated brain spectra in …

… learning that uses softmax to implicitly capture the relations … upsampling blocks use the
U-Net’s fundamental concepts to … To assess the performance of the regression task, we adopt …

… of brain dynamics or behavior into a statistical machine … of the system, like the connection
(or regression) weights, are … computationally based assessment of behavior [167], on their own …

… the methods of using the deep learning regression model to assess … and virtual reality for
mental health physical examination. … network, we introduce residual connection to the module. …

… often utilized because of the greater determination [7]. While … as a rule, can see the connection
with the normal tissue. MR … a softmax regression classifier and Deep autoencoder. BFC …

… to max depth = 3, number of estimators = 100, and learning rate = … Brain age gap was
calculated using estimated brain age … We then ran a linear regression on the continuous measure …