What are the main challenges in developing neural networks?

What are the main challenges in developing neural networks?

Some key challenges include overfitting, data quality, computational resources, and model interpretability.

How can we address overfitting in neural networks?

Techniques like regularization, dropout, and using more training data can help mitigate overfitting.

What role does data quality play in neural network performance?

Data quality is crucial; poor quality data can lead to inaccurate models and unreliable predictions.

Can you explain model interpretability and why it's important?

Model interpretability helps us understand how decisions are made, which is vital for trust and accountability in AI.