Gradient Descent Explained MCQ | Optimization for AI Multiple Choice Questions

Direction: Choose the correct option

What is gradient descent?

A. An optimization algorithm

B. A regression algorithm

C. A classification algorithm

D. A clustering algorithm

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In gradient descent, the negative gradient points in the direction of:

A. Steepest descent

B. Random direction

C. No change

D. Steepest ascent

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What is the update rule for parameter θ?

A. θ = θ - α * ∇J(θ)

B. θ = α * ∇J(θ)

C. θ = ∇J(θ)

D. θ = θ + α * ∇J(θ)

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Batch gradient descent uses:

A. Entire dataset per update

B. One sample per update

C. Mini-batch per update

D. None

Direction: Choose the correct option

Stochastic gradient descent (SGD) updates parameters:

A. After each training example

B. After each epoch

C. After each batch

D. Once

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