Understanding the Math behind Self Attention Mechanism

In the series – ‘Key Concepts for Transformers,’ I have covered Tokens, Tokenization, and Word Embeddings, Self Attention Mechanism so far. In this post, I will explain the maths behind the ‘Self-Attention Mechanism.’. In this post we will cover the following:

1. Flowchart Depiction of Self-Attention Mechanism:
– We’ll start with a visual flowchart that outlines the step-by-step process of the self-attention mechanism.

2. Detailed Explanation of Each Section:
– Each component of the self-attention mechanism will be explained in detail across a series of pages.

3. Basic Example Illustration:
– In the final section, we’ll solidify our understanding with a basic example.

As we approach the unveiling of the Transformer architecture, these posts aim to build a solid foundation in understanding how Transformers revolutionized NLP tasks. By grasping the underlying mathematics, you’ll be better equipped to appreciate the elegance and power of this architecture.

Stay tuned for the next post, where we’ll explore the Encoder and Decoder structures that make up the Transformer model.

I hope you are following it through, and finding it beneficial 😊

#GenAI #AI #DataScience, #LLM #Transformers #Selfattention.