What is transformer architecture in AI?

What is transformer architecture in AI?

Transformer architecture is a deep learning model designed for natural language processing tasks, introduced in the paper 'Attention is All You Need'.

How does it differ from RNNs?

Unlike RNNs, transformers process all input data simultaneously using self-attention mechanisms, making them faster and more efficient.

What are the key components of a transformer?

The key components include the encoder, decoder, multi-head attention, and feed-forward neural networks.

Can you explain self-attention?

Self-attention allows the model to weigh the importance of different words in a sentence relative to each other, enhancing context understanding.

What are some applications of transformer architecture?

Transformers are widely used in machine translation, text summarization, and even image processing tasks.