How can I effectively scale deep learning models?

How can I effectively scale deep learning models?

Scaling deep learning models involves optimizing architecture, data, and training processes. What specific aspect are you interested in?

I'm curious about data scaling techniques.

Data scaling techniques include normalization, augmentation, and using larger datasets. Would you like to know more about any specific technique?

Yes, tell me about data augmentation.

Data augmentation involves creating modified versions of your training data to improve model generalization. Techniques include rotation, flipping, and adding noise.

How do I implement these techniques in my code?

You can use libraries like TensorFlow or PyTorch, which have built-in functions for data augmentation. Would you like code examples?