📚 node [[exploding_gradient_problem|exploding gradient problem]]
Welcome! Nobody has contributed anything to 'exploding_gradient_problem|exploding gradient problem' yet. You can:
  • Write something in the document below!
    • There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
  • Write to the Agora from social media.
    • If you follow Agora bot on a supported platform and include the wikilink [[exploding_gradient_problem|exploding gradient problem]] in a post, the Agora will link it here and optionally integrate your writing.
  • Sign up as a full Agora user.
    • As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)
⥅ related node [[exploding_gradient_problem]]
⥅ node [[exploding_gradient_problem]] pulled by Agora

exploding gradient problem

Go back to the [[AI Glossary]]

#seq

The tendency for gradients in a deep neural networks (especially recurrent neural networks) to become surprisingly steep (high). Steep gradients result in very large updates to the weights of each node in a deep neural network.

Models suffering from the exploding gradient problem become difficult or impossible to train. Gradient clipping can mitigate this problem.

Compare to vanishing gradient problem.

F

📖 stoas
⥱ context