Consider transformer networks. With their large number of parameters, how are they typically trained to avoid overfitting, especially in natural language processing tasks?
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what is overfitting Informational
- How does the choice of optimizer affect the implicit regularization of deep neural networks?
- Can adversarial training help improve the robustness and generalization of models?
- Is it always possible to completely eliminate overfitting, or is some level of overfitting often a trade-off for achieving good performance on the training data?
- How can we develop better theoretical frameworks for understanding generalization in deep learning?
- What are some techniques to prevent the discriminator in a GAN from overfitting, such as using dropout and adding noise to the inputs?
- How to report the presence and extent of overfitting in model evaluation?
- What are some techniques for generating synthetic data to combat overfitting?
- Imagine you have a model that achieves perfect accuracy on the training data but performs poorly on the validation data. What does this strongly suggest, and what are your next steps?
- What are the educational resources available for learning more about overfitting and related concepts?
- What are some specific regularization techniques used in convolutional neural networks (CNNs)?
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