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 and ensures better training results.

What about choosing the right optimizer?

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

Should I use regularization techniques?

Yes, regularization techniques like dropout or L2 regularization can help prevent overfitting.

How do I know if my model is overfitting?

You can monitor the training and validation loss; if the training loss decreases while validation loss increases, it's a sign of overfitting.