# Transformers And Attention Mechanisms Explained

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Transformers are a neural‑network architecture based on a self‑attention mechanism pioneered by Google and they took the machine‑learning world by storm in 2021.

I highlighted this in a LinkedIn post on 2022‑01‑27, tagging @Nathan and @ian and urging readers to check the State of AI Report 2021 for deeper context.

RNNs were not capable enough to capture long range dependencies effectively.

Self‑attention can capture dependencies between any two words in an input sequence via a scoring mechanism and compute the relevance of each word in a context.

RNNs suffer from vanishing or exploding gradients as the sequence becomes larger.

I recommend a beginner‑friendly tutorial that walks through the birth of transformers in a simple narrative.

The tutorial is hosted at https://lnkd.in/dTPSs_yV.

It is presented by Hila Chefer and Sayak Paul.

The narrative chain is RNNs → Attention is all you need → Transformers → Depth around Vision.

The post was made on 2023‑06‑20 13:26 UTC and used 5 hashtags.

I decided to use additive positional encodings (vector addition) rather than concatenation, keeping dimensionality unchanged and allowing reuse of pretrained weights.

I recorded the observation timestamp 2023-07-25 14:00 UTC for the note on positional encodings.

When tokenized with WordPiece, the sentence “I enjoy studying astrophysics.” becomes the token list ["I", "enjoy", "studying", "astro", "physics"].

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal