ML Knowledge

How would you handle the vanishing gradient problem and exploding gradient problem?

Data Scientist

Amazon

Confluent

WeWork

Tinder

Intuit

Rippling

Did you come across this question in an interview?

  • How would you handle the vanishing gradient problem and exploding gradient problem?
  • It's a well-known issue that gradient problems can hamper deep learning productivity. How do you usually handle them?
  • Gradient issues can be frustrating in deep learning. What are your tried-and-tested techniques to manage these problems?
  • As a machine learning expert, can you share some of your insights on how to deal with gradient problems, particularly the vanishing and exploding type?
  • How do you approach gradient issues in deep learning, particularly the vanishing and exploding problems?
  • As a practitioner in deep learning, what steps do you typically take to overcome vanishing and exploding problems in gradient descent?
  • When faced with gradient problems, how do you modify your deep learning algorithms to ensure stable and accurate predictions?
  • Gradient issues can be a major roadblock in deep learning. Can you describe your process of mitigating these problems?
  • What steps do you take to deal with gradient problems in deep learning, and how do you evaluate their effectiveness?
  • What are your strategies to tackle the vanishing or exploding gradient problem in deep learning?

Interview question asked to Data Scientists interviewing at WeWork, NetSuite, NetApp and others: How would you handle the vanishing gradient problem and exploding gradient problem?.