Explain the concept of Gradient Descent in optimization.
This question evaluates your understanding of the fundamental algorithm used to minimize loss functions in machine learning models.
Why Interviewers Ask This
Gradient Descent is the engine behind most ML training. Interviewers ask this to ensure you understand how models learn from data. They want to know if you grasp the mechanics of updating weights based on the gradient of the loss function.
How to Answer This Question
Define Gradient Descent as an iterative optimization algorithm to minimize a function. Explain that it moves in the direction of the steepest descent (negative gradient) to find the minimum. Discuss the role of the learning rate and mention variants like Stochastic Gradient Descent (SGD) or Mini-batch GD.
Key Points to Cover
- Iteratively updates weights to minimize loss.
- Moves in the direction of negative gradient.
- Learning rate controls the step size.
- Variants include SGD and Mini-batch GD.
Sample Answer
Gradient Descent is an iterative optimization algorithm used to minimize the loss function by adjusting model parameters. It works by calculating the gradient of the loss function with respect to the weights and updating…
Common Mistakes to Avoid
- Confusing gradient with derivative in multivariate contexts.
- Not mentioning the learning rate.
- Failing to distinguish between local and global minima.
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