The loss function reflects the error between the target output and actual output of the neural network. The commonly used loss function in deep learning is:

Question

Options

• AExponential loss function
• BMean square loss function
• CLog loss function
• DHinge Loss function

• A
  14% (5)
• B
  78% (29)
• C
  3% (1)
• D
  5% (2)

Explanation

Mean Square Error (MSE) loss is the foundational and most widely taught loss function in deep learning, measuring the average of squared differences between predicted and target values, making it directly interpretable and mathematically convenient for gradient-based optimization in regression tasks. Exponential loss (A) is primarily associated with boosting ensemble methods like AdaBoost rather than neural networks trained via backpropagation. Log loss (C), also known as cross-entropy, is common for classification tasks but is more specialized than MSE and is not the canonical general-purpose answer in introductory deep learning curricula. Hinge loss (D) is the hallmark loss function of Support Vector Machines, not neural networks. Memory tip: Think "Mean Square" as the "Most Standard" loss - it is the first loss function introduced in virtually every deep learning textbook because its smooth, differentiable surface is ideal for illustrating how gradient descent minimizes error.

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