A to Z of Transformer Architecture: A Comprehensive Learning Series

I am sharing a series of documents that will help to understand Transformer Architecture from end to end, and in easy to understand language.

Transformer Architecture remains the backbone of Generative AI LLM Models. Understanding this will help you a lot to understand other concepts in the field. Please follow through the individual documents and learn/ refresh your concepts. I hope the content will help you.

Feel free to save this, and take advantage of learning in free time 😊

𝐏𝐚𝐫𝐭 𝟏: 𝐓𝐨𝐤𝐞𝐧𝐬 𝐚𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧: https://lnkd.in/g-NX8m3c

-Understand tokens, their creation, methods, examples

𝐏𝐚𝐫𝐭 𝟐: 𝐖𝐨𝐫𝐝 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 : https://lnkd.in/gifb_E85

-Understand word embeddings: concept, example and creation steps

𝐏𝐚𝐫𝐭 𝟑: 𝐒𝐞𝐥𝐟 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦: https://lnkd.in/gncrUEwd

-Examples demonstrate how self-attention differentiates same words in various sentence contexts.

𝐏𝐚𝐫𝐭 𝟒: 𝐌𝐚𝐭𝐡𝐬 𝐁𝐞𝐡𝐢𝐧𝐝 𝐒𝐞𝐥𝐟 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦: https://lnkd.in/gm5pZEX9

-This resource provides a visual flowchart of the self-attention mechanism, a detailed explanation of each component, and a simple example for clarity.

𝐏𝐚𝐫𝐭 𝟓: 𝐌𝐮𝐥𝐭𝐢-𝐇𝐞𝐚𝐝 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧: https://lnkd.in/gdAQ4WHP

-This guide explains the concept of Multi-Head Attention, a powerful mechanism, with a step-by-step breakdown using a relatable example.

𝐏𝐚𝐫𝐭 𝟔: 𝐄𝐧𝐜𝐨𝐝𝐞𝐫𝐬: https://lnkd.in/gFU8KgDW

-Visualize how a Transformer Encoder works, learn its basic function, and understand each step with examples.

𝐏𝐚𝐫𝐭 𝟕: 𝐃𝐞𝐜𝐨𝐝𝐞𝐫𝐬: https://lnkd.in/gDtEpyqu

-Start by visualizing the Decoder’s structure and process with a flowchart.

Happy Learning 🙂