Explanation

Let's analyze each statement:

Statement A: True

• A neural network without activation functions becomes a linear transformation, equivalent to linear regression.

Statement B: True

• A neural network with no hidden layers (just input to output) is indeed equivalent to linear regression.

Statement C: True

• The bias term in neural networks serves the same purpose as the constant/intercept term in regression models.

Statement D: False

• This statement is incorrect because:
  • Overfitting is NOT avoided by calculating validation data simultaneously with training data
  • In fact, calculating validation data during training is standard practice to monitor overfitting
  • Overfitting is typically avoided through techniques like:
    • Early stopping (monitoring validation loss)
    • Regularization (L1/L2)
    • Dropout
    • Cross-validation
    • Proper train/validation/test split

Therefore, statement D is the false statement as it misrepresents how overfitting is prevented in neural networks.