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What are your strategies to tackle the vanishing or exploding gradient problem in deep learning?

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Answers

Anonymous

4Strong
Vanishing and exploding gradients usually occur in sequential models where the weights are share between input values. As the weights are shared, if the value is greater than 1 that means it will keep multiply with itself until the end of the sequence which causes exploding gradients or inf or nan values. On the other hand, when value is less than 1, the wieght can go to 0 which is vanishing gradients. We can use a few strategies to combat that. For example, we use a gated architecture in gru and lstm to refresh the sequence weights when the words are not related to each other. Then, we can use gradient clipping which decides the max and min value and if value goes above that, it clips the gradient. Then, we can normalize the gradient values to keep the values in a certain range, let's say 0 and 1. Then, we can use some kind of layer, batch or instance normalization to normalize the values. 

Anonymous

3Strong
Gradient clipping to help deal with exploding gradients, residual connections to deal with vanishing gradients
  • What are your strategies to tackle the vanishing or exploding gradient problem in deep learning?
  • 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?
  • How would you handle the vanishing gradient problem and exploding gradient problem?
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Interview question asked to Machine Learning Engineers interviewing at Zillow, Illumina, Mailchimp and others: What are your strategies to tackle the vanishing or exploding gradient problem in deep learning?.