What are the differences between transformer models and convolutional networks?

What are the differences between transformer models and convolutional networks?

Transformer models are designed for sequential data and excel in tasks like NLP, while convolutional networks are great for image processing.

Can you explain how transformers handle sequential data?

Transformers use self-attention mechanisms to weigh the importance of different words in a sequence, allowing them to capture context effectively.

What are some applications of convolutional networks?

Convolutional networks are widely used in image classification, object detection, and medical image analysis.

Which model is better for text generation?

Transformers are generally better for text generation due to their ability to understand context and generate coherent text.