Inside Transformers with Attention is All you Need

By Dr. Wasim Ahmad Khan

How do models like ChatGPT, Google Translate, and BERT actually understand language so well?

The answer for all of them is the Transformer.

Why Attention is all you need?

That's Why Attention is all you need?

You don't need Recurrence.

You don't need Convolutions.

Attention is all you need.

The Problem: Sequential Models

Older models like RNNs (Recurrent Neural Networks) processed text word-by-word, in order.

Limitation 1: The Sequential Bottleneck

You can't process word 5 until you've processed word 4. This is slow and cannot be parallelized on modern GPUs.

Limitation 2: Long-Range Dependency

Information from the start of a long sentence gets "lost" by the time the model reaches the end.

e.g., "The cat, which chased the dog... all the way to the park, ...was fluffy."

Neural Network Timeline

ANN

1943

McCulloch & Pitts (1943)

RNN

1986

Rumelhart et al. (1986)

LSTM

1997

Hochreiter & Schmidhuber (1997)

CNN

1998

LeCun et al. (1998)

GRU

2014

Cho et al. (2014)

Transformers

2017

Vaswani et al. (2017)

Introduction

The Full Architecture (Overview)

This is the complete model from the paper. It uses a stack of Encoders and Decoders.

The Encoder (Left Side)

Its job is to "understand" the input sentence.

The Decoder (Right Side)

Its job is to "generate" the output sentence.

Now, let's break down the components inside each of these blocks.

Input Embeddings

Before anything else, we must convert words into numbers the model can understand.

Words are converted into high-dimensional numbers (vectors).

Think of it like giving each word a unique ID badge, but one that also has coordinates showing how similar it is to other words.

"King" and "Queen" would be close together.
"King" and "Apple" would be far apart.

Positional Encoding

A New Problem: Self-Attention (which we'll see) looks at all words at once. It has no idea about word order.

To the model, "The man bites the dog" and "The dog bites the man" look identical!

The Solution:

We create a unique "Positional Encoding" vector for each position (1st, 2nd, 3rd...).

This vector is added to the word's embedding, giving the model a signal for where each word is in the sequence.

Positional Encoding Visualization

The Idea of Self-Attention

The 2017 paper "Attention Is All You Need" got rid of recurrence entirely.

The solution is Self-Attention: A mechanism that lets the model look at all other words in the sentence at the same time when processing a single word.

Analogy:

"The animal didn't cross the street because it was too tired."

Attention instantly links "it" back to "animal", no matter the distance.

Self-Attention Visualization

How Self-Attention Works

For each word, we create three vectors:

Query (Q): "What I am looking for."
Key (K): "What I contain." (A label)
Value (V): "What I actually am." (The content)

The Process: (Like a library)

Score: Compare your Q (search query) to every word's K (book title).
Scale: (A math step to keep numbers stable).
Weights (Softmax): Turn scores into probabilities.
Output: Combine all V's (book contents) based on these weights.

Multi-Head Attention

Why use just one set of Q, K, V?

A word can have multiple relationships. In "The cat sat on the mat":

"sat" relates to "cat" (who sat)
"sat" relates to "mat" (where it sat)

Solution: Multi-Head Attention (e.g., 8 heads)

It's like having 8 different "experts" (heads) look at the sentence in parallel. Each head learns a different kind of relationship.

The results from all heads are combined to get a final, rich representation.

Feed-Forward Network

After the attention step, the model needs to "process" the information it has gathered.

After attention, each word's new vector (rich with context) is passed through a simple, standard neural network.

This is like the chef mixing the ingredients after gathering them.

This step gives the model more power and allows it to learn more complex patterns from the context-rich vectors.

Neural Network Visualization

Residuals & Layer Norm

These two components are the "glue" that holds the model together and allows it to be trained successfully.

Residual Connections

This is a "shortcut." We add the *original* input vector to the *output* of the attention/FFN layer.

This prevents information from being lost as it goes through many layers.

Layer Normalization

This keeps the numbers (vectors) stable and under control during training.

Think of it like safety rails on a highway, preventing the model's calculations from "going off track."

Residual Connection Visualization

Step-by-Step Example:

Translating "I love cats"

Input: "I love cats" (Converted to Embeddings).
Position Info: Add vectors for 1st, 2nd, 3rd position.
Encoder: The stack of Encoder layers processes this, creating a rich understanding of the sentence.
Decoder Starts: The Decoder begins generating the output, word by word.
Decoder (Word 1): It pays attention to "I" and generates "Je".
Decoder (Word 2): It pays attention to "love" and generates "aime".
Decoder (Word 3): It pays attention to "cats" and generates "les chats".
Result: "Je aime les chats"

7. Why It Won: The Impact

Massive Parallelization: Calculations can be done all at once (matrix math), not word-by-word. This is perfect for GPUs and much faster to train.
Superior Context: The attention mechanism provides a direct path between any two words, solving the long-range dependency problem.
Foundation for Modern AI: BERT (Encoders), GPT (Decoders), and T5 (Full Model) are all built on this architecture.

Conclusion & Q&A

Summary of Key Points:

Transformers replaced recurrence (RNNs) with attention.
The core idea is Self-Attention (Query, Key, Value).
Multi-Head Attention learns different relationships in parallel.
Positional Encoding adds the word order information back.
This design is faster (parallel) and smarter (context).