What are the best practices for training neural networks?

What are the best practices for training neural networks?

Some best practices include using a proper dataset, normalizing your data, and choosing the right architecture.

How important is data preprocessing?

Data preprocessing is crucial as it helps improve the model's performance by ensuring the data is clean and well-structured.

What about choosing the right optimizer?

Choosing the right optimizer can significantly affect training speed and convergence. Popular choices include Adam and SGD.

How do I avoid overfitting?

To avoid overfitting, you can use techniques like dropout, regularization, and ensuring you have enough training data.

Can you explain the role of learning rate?

The learning rate determines how much to change the model in response to the estimated error each time the model weights are updated.