Attention mechanism transformer

An attention mechanism in deep learning allows a model to focus on relevant parts of the input when processing data. The Transformer is a popular neural network architecture that uses attention as a key component. Here are some key points:

The Transformer was first proposed in 2017 for machine translation, outperforming older recurrent models.
It relies entirely on self-attention to draw global dependencies between input and output unlike RNNs or CNNs.
The multi-head attention block is the core building unit. It calculates attention weights signifying importance given to parts of the input.
The weights are used to extract relevant information and give higher priority to important words/tokens when generating outputs.
This allows the model to focus on specific parts of long input sequences to generate predictions, without regard to position.
Residual connections and layer normalization components allow stable training of deep Transformer models.
Transformers have become ubiquitous in NLP, achieving state-of-the-art results in translation, text generation, classification and other language tasks.
They are also gaining popularity in computer vision, speech, and even general machine learning problems.

The attention mechanism and Transformer architecture facilitate modeling of global context and long-range dependencies in data efficiently.