Answer: Overfitting is avoided by carrying out calculations for the validation data set at the same time as the training data set.

## Explanation Let's evaluate each statement: - **A**: **True** - Without activation functions, neural networks become linear transformations, essentially equivalent to linear regression. - **B**: **True** - A neural network with no hidden layers (just input and output layers) and linear activation functions is equivalent to linear regression. - **C**: **True** - The bias term in neural networks serves the same purpose as the intercept/constant term in regression models. - **D**: **FALSE** - Overfitting is typically avoided by using separate training and validation datasets, not by calculating them simultaneously. The validation set is used to monitor performance during training and prevent overfitting by stopping training when validation performance starts to degrade (early stopping). Therefore, statement D is false because carrying out calculations for training and validation datasets simultaneously doesn't prevent overfitting; proper separation and monitoring are required.

Author: LeetQuiz Editorial Team