H13-311_V3.5 · Question #51
In the process of deep learning model training, what are the common optimizers?
The correct answer is A. Adam B. Adagrad C. SGD D. Momentum. All four options are correct because Adam, Adagrad, SGD, and Momentum are all well-established optimizers widely used in deep learning model training. SGD (Stochastic Gradient Descent) is the foundational optimizer that updates weights using gradients computed on small random…
Question
In the process of deep learning model training, what are the common optimizers?
Options
- AAdam
- BAdagrad
- CSGD
- DMomentum
How the community answered
(31 responses)- A100% (31)
Explanation
All four options are correct because Adam, Adagrad, SGD, and Momentum are all well-established optimizers widely used in deep learning model training. SGD (Stochastic Gradient Descent) is the foundational optimizer that updates weights using gradients computed on small random batches, while Momentum extends SGD by accumulating a velocity term that accelerates convergence in consistent gradient directions. Adagrad adapts the learning rate for each parameter based on historical gradient magnitudes, making it well-suited for sparse data, and Adam (Adaptive Moment Estimation) combines the ideas of Momentum and RMSProp, maintaining both first and second moment estimates of gradients, which makes it one of the most popular choices in practice. Since this is an all-correct multiple-choice question, there are no incorrect distractors to rule out.
Memory tip: Use the phrase "Some Marvelous Algorithms Arrive" to recall SGD, Momentum, Adam, and Adagrad -- each initial maps to a key optimizer family, from simple gradient descent to fully adaptive methods.
Topics
Community Discussion
No community discussion yet for this question.