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How does stochastic gradient descent converge, and what makes it advantageous compared to traditional gradient descent?

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Answers

Anonymous

2 months ago
4Strong
Due to the randomness of the method, we randomly select a subset of samples (a batch), the method in general will not find a point where the gradient is 0, i.e. a minima. However, the model is likely to land in an area near a minima. The advantages of this approach are: (1)for the massive datasets of deep learning, it is unfeasible to compute the gradient considering all the samples (the time to compute one update step would be extremely high as we would have to iterate over the entire dataset), (2) the randomness prevents the model from getting stuck on a local minima (since we are considering only a small set of samples the batch gradient vector does not coincide with the gradient vector and for this reason will not get stuck in local minima), (3) it makes the model less sensitive to the initialization  (in the non-stochastic case, the gradient is deterministic and will push the model to the closest local minima) and (4) it helps to prevent overfitting (it is harder for the model to overfit since it is being update only a subset of samples)..  
  • How does stochastic gradient descent converge, and what makes it advantageous compared to traditional gradient descent?
  • What are the benefits of SGD's convergence properties over those of standard gradient descent?
  • Could you compare the convergence process of SGD to gradient descent and highlight its strengths?
  • What distinguishes the convergence of stochastic gradient descent from that of gradient descent?
  • How does the convergence mechanism of SGD provide benefits over that of regular gradient descent?
  • Can you outline the convergence behavior of stochastic gradient descent and how it improves upon gradient descent?
  • What aspects of SGD's convergence make it superior to conventional gradient descent?
  • Why does SGD converge differently than gradient descent, and what advantages does this present?
  • How would you describe the advantages of SGD's convergence over that of gradient descent in optimization problems?
  • Can you discuss the convergence of stochastic gradient descent (SGD) and its advantages over gradient descent?
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Interview question asked to Machine Learning Engineers interviewing at IBM, eBay, Epic Games and others: How does stochastic gradient descent converge, and what makes it advantageous compared to traditional gradient descent?.