How Attention Mechanism Works in Transformer Architecture

#llm #embedding #gpt The attention mechanism in transformers is a key component that allows models to focus on different parts of an input sequence when making predictions. Attention assigns varying degrees of importance to different parts of the input, enabling the model to capture contextual relationships effectively. The most widely used form of attention in transformers is self-attention, where each token in a sequence attends to all other tokens, capturing long-range dependencies. This mechanism is further enhanced by multi-head attention, which enables the model to focus on multiple aspects of the data simultaneously. There are several types of attention mechanisms, including self-attention, which is used in transformers to relate different words in the same sentence, and cross-attention, which is commonly seen in tasks like machine translation where the model attends to a separate input sequence. In the video, I have visually explained each of these attention mechanisms with clear animations and step-by-step breakdowns. Timestamps: 0:00 - Embedding and Attention 2:12 - Self Attention Mechanism 10:52 - Causal Self Attention 14:12 - Multi Head Attention 16:50 - Attention in Transformer Architecture 17:54 - GPT-2 Model 21:30 - Outro Attention is all you need paper: https://arxiv.org/abs/1706.03762 Music by Vincent Rubinetti Download the music on Bandcamp: https://vincerubinetti.bandcamp.com Stream the music on Spotify: https://open.spotify.com/artist/2SRhEEt2tlDQWxzwfUo9Dl