What are some common issues in deep learning?

What are some common issues in deep learning?

Some common issues in deep learning include overfitting, underfitting, vanishing gradients, and data imbalance.

Can you explain overfitting and how to prevent it?

Overfitting occurs when a model learns the training data too well, including noise. To prevent it, you can use techniques like regularization, dropout, and early stopping.

What is data imbalance and why is it a problem?

Data imbalance happens when one class of data is significantly more represented than others, leading to biased models. It can be addressed by resampling techniques or using different evaluation metrics.

How do vanishing gradients affect training?

Vanishing gradients make it difficult for the model to learn, especially in deep networks, as the gradients become too small to update weights effectively. Using activation functions like ReLU can help mitigate this.

What are some best practices to avoid these issues?

Best practices include using cross-validation, monitoring training performance, employing data augmentation, and selecting appropriate model architectures.