What are some common challenges in training deep neural networks?
Choosing the right architecture and hyperparameters for your neural network can also be challenging. It often requires experimentation and trial-and-error to find what works best for your specific problem.
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One common challenge is overfitting, where the model performs well on the training data but fails to generalize to unseen data. Regularization techniques like dropout and weight decay can help combat overfitting.
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Training deep neural networks can be computationally expensive, especially for large models and datasets. Utilizing techniques like distributed training or using hardware accelerators like GPUs can help speed up the training process.
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Another challenge is vanishing/exploding gradients, which can make it difficult for the model to learn. Techniques like gradient clipping and using different activation functions can help alleviate this issue.
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A lack of labeled data can pose another challenge, especially in domains where acquiring labeled data is expensive or time-consuming. Techniques like transfer learning and data augmentation can help mitigate this issue.
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