Attention in transformers, step-by-step | Deep Learning Chapter 6

Video: https://www.youtube.com/watch?v=eMlx5fFNoYc

• Introduction to Transformers and Attention ([00:00])

• Recap of the previous chapter's exploration of transformers

• Introduction to the 2017 paper "Attention is All You Need"

• High-dimensional token embeddings and their semantic meanings

• Understanding Contextual Meaning Through Attention ([02:30])

• Examples showcasing different meanings of words like "mole" in context

• Mechanism of updating embeddings based on surrounding context

• Process of Attention in Transformers ([05:00])

• Introduction to single head of attention and position encoding

• Use of matrices for query, key, and value in computing attention

• Computing Attention Patterns ([08:00])

• Explanation of dot products between keys and queries

• Use of softmax function to normalize attention scores

• Masking and Context Size Challenges ([11:30])

• Discussion on masking during training to prevent future token influence

• Explanation of context size limitations and scalability issues

• Updating Embeddings with Value Vectors ([15:00])

• Process of using value matrices to update embeddings

• Concept of multi-headed attention and its advantages

• Parameterization of Attention Heads ([18:00])

• Breakdown of parameters in attention heads using GPT-3 as an example

• Explanation of value matrix factorization for efficiency

• Conceptual Overview of Multi-Headed Attention ([21:30])

• Multi-headed attention allows learning of diverse contextual updates

• Importance of running multiple attention heads in parallel

• Expanding Transformer Networks ([24:00])

• Overview of how attention blocks fit within a larger transformer network

• Discussion on the parallelizable nature of attention mechanisms

• Future Insights and Resources ([27:00])

• Mention of additional resources for learning about attention in transformers

• Importance of parallelization in deep learning advancements