A machine learning engineer is training a deep neural network on a massive dataset characterized by a highly non-convex loss surface. The engineer has chosen to use Stochastic Gradient Descent (SGD) instead of Batch Gradient Descent (BGD). Which statement best explains a key advantage of SGD in this specific context?
SGD guarantees a faster and more stable convergence to the global minimum by avoiding the noisy gradients associated with BGD.
SGD reduces the learning rate automatically during training, which leads to a more direct path towards the minimum of the loss function.
The parameter updates in SGD are computationally heavier per epoch and provide a more accurate gradient estimation than BGD.
The high variance in parameter updates, resulting from using a single sample, can help the model escape shallow local minima.
The correct answer explains that the high variance in parameter updates, a core feature of Stochastic Gradient Descent (SGD), is advantageous for navigating complex, non-convex loss surfaces. In SGD, the gradient is calculated based on a single training sample for each parameter update. This approach introduces significant noise or 'stochasticity' into the optimization process. This noise allows the optimizer to 'jump' out of shallow local minima, which are common in deep learning, and explore the parameter space more broadly, increasing the chances of finding a more optimal solution.
The distractor claiming SGD guarantees faster, more stable convergence to a global minimum is incorrect. SGD's convergence path is notoriously noisy and oscillatory, not stable, and while it often helps find good minima in non-convex problems, it offers no guarantees of finding the global minimum.
The distractor stating SGD updates are computationally heavier is the opposite of the truth. SGD is computationally light per update because it only processes one sample, whereas Batch Gradient Descent (BGD) must process the entire dataset, making its updates much more expensive.
The distractor suggesting SGD automatically reduces the learning rate is also incorrect. While using a learning rate schedule (a technique of reducing the learning rate over time) is a common and recommended practice when using SGD, it is a separate, complementary mechanism and not an inherent feature of the SGD algorithm itself.
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Why does the high variance in SGD parameter updates help escape shallow local minima?
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What is the difference between SGD and Batch Gradient Descent (BGD)?
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What role does a learning rate schedule play in SGD?