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

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  • 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?.