CS9 - Understanding The Encoder 🤖 (Part II)

Decoding the Encoder: A Deep Dive into Transformer Architecture

This article is the second part of a three-part deep dive into one of the most revolutionary AI architectures of our time:

Transformers.

Here’s what’s coming your way:

✅ Week 1: Understanding the Transformers architecture → Link
✅ Week 2: Understanding The Encoder → Today!
⏳ Week 3: Understanding The Decoder → Available on 30th March ‼️

Understanding the Encoder - Part II

The encoder is a fundamental component of the Transformer architecture.

The primary function of the encoder is:

To transform the input tokens into contextualized representations.

Unlike earlier models that processed tokens independently, the Transformer encoder captures the context of each token in relation to the entire sequence.

Its structure consists of the following elements:

Encoder’s architecture.

• Multi-Head Self-Attention Layer

• Layer Normalization (applied twice per layer)

• Feed-Forward Neural Network

Before starting, here you have the full-resolution cheatsheet 👇🏻