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Transformers — The Architecture That Changed AI
A scroll-driven visual deep dive into the Transformer architecture. From RNNs to self-attention to GPT — understand the engine behind every modern AI model.
Introduction
🎯 0/5 0%
🔮
One paper changed
everything.
In 2017, Google published “Attention Is All You Need.”
It introduced the Transformer — the architecture behind
ChatGPT, Claude, Gemini, DALL-E, AlphaFold, and basically every AI breakthrough since.
↓ Scroll to learn — this one’s a ride
Before Transformers
The Dark Ages: RNNs and Their Limits
Before Transformers, we had Recurrent Neural Networks (RNNs) and their fancier cousin, LSTMs. They processed sequences one word at a time, in order — like reading a book where you can only see one word at a time.
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🟢 Quick Check Knowledge Check
What was the biggest limitation of RNNs that Transformers solved?
💡 Think about what 'recurrent' means — what does each step depend on?
RNNs process tokens one-by-one in order — token t+1 must wait for token t. This makes them (1) slow to train because you can't parallelize, and (2) bad at long-range dependencies because information degrades over many sequential steps. Transformers process all tokens in parallel.
Self-Attention
💡 The Core Innovation: Self-Attention
What if every word could look at every other word, all at the same time?
No waiting. No sequential bottleneck. Full context, instantly.
Query, Key, Value — The Heart of Attention
Every token gets turned into three vectors. Think of it as three questions every word asks:
The attention mechanism
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Q = XWq (Query: 'What am I looking for?') Each word creates a search query — what information does it need from other words?
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K = XWk (Key: 'What do I contain?') Each word creates a key — what kind of information does it offer to others?
3
V = XWv (Value: 'Here's my info') Each word creates a value — the actual information it wants to share
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Attention(Q,K,V) = softmax(QKᵀ/√d)V Compare every Q with every K (dot product), normalize with softmax, then mix the Values accordingly
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🟡 Checkpoint Knowledge Check
In self-attention, each token produces Q, K, V vectors. What does the dot product of Q and K represent?
💡 Q asks a question, K provides an answer — how do you measure how well they match?
The dot product Q·K measures how 'similar' or 'relevant' two tokens are to each other. High dot product = high relevance = more attention. This score gets normalized by softmax so each token's attention weights sum to 1.
Multi-Head Attention
Multi-Head Attention: Looking at Things from Different Angles
One attention head captures one type of relationship — maybe syntactic (subject-verb). But sentences have MANY types of relationships.
Multi-head attention splits the work
Split Dimensions
The model's full dimension (e.g., 512) is divided equally across h heads (e.g., 8). Each head works with a smaller slice (d_k = 64), making it computationally efficient while allowing specialization.
d_k = d_model / h = 512/8 = 64 Independent Heads
Each head has its own learned projection matrices for Q, K, and V. This lets different heads specialize in different relationship types — one might learn syntax, another semantics, another coreference.
head_i = Attention(QWᵢQ, KWᵢK, VWᵢV) Concatenate & Project
All head outputs are concatenated back into the full dimension, then passed through a final linear projection. This combines the diverse insights from all heads into a single rich representation.
MultiHead = Concat(head_1, ..., head_h)Wᴼ Architecture
The Full Transformer Block
Self-attention is just one piece. A complete Transformer block has two sub-layers, each with a residual connection and layer normalization.
Encoder-Decoder vs Decoder-Only
The original Transformer had both an encoder (understand the input) and a decoder (generate the output). But modern LLMs simplified this:
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🟡 Checkpoint Knowledge Check
Why do modern LLMs like GPT and LLaMA use decoder-only architecture?
💡 Think about simplicity and scaling — what happens when you want to go from 1B to 100B parameters?
Decoder-only Transformers handle input understanding and output generation with the same stack of layers. This simplicity makes scaling easier — just add more layers and data. The causal masking (each token only sees previous tokens) naturally supports autoregressive generation.
Positional Encoding
Wait — How Does It Know Word Order?
Here’s a subtle but critical problem: self-attention treats all positions equally. “Dog bites man” and “Man bites dog” would produce the same attention scores! We need to inject position information.
Positional encoding — telling the model WHERE each word is
Sine Component
Even-numbered dimensions use sine waves at different frequencies. Each position in the sequence gets a unique sine pattern, like a fingerprint. Lower dimensions oscillate slowly (capturing coarse position), higher dimensions oscillate fast (fine position).
PE(pos, 2i) = sin(pos / 10000^(2i/d)) Cosine Component
Odd-numbered dimensions use cosine waves at the same frequencies. Together with the sine components, they create a unique, distinguishable encoding for every position — and the model can learn relative positions from the geometric relationship between any two encodings.
PE(pos, 2i+1) = cos(pos / 10000^(2i/d)) Addition to Embedding
The positional encoding is simply added to the word embedding vector. The model learns to disentangle the two signals through training, gaining both semantic meaning and position awareness in a single vector.
input = embedding + PE Training
How Transformers Learn
Training a Transformer is surprisingly simple in concept:
The training recipe
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Step 1: Mask out future tokens During training, the model sees a sentence but each position can only look at previous positions (causal masking)
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Step 2: Predict next token at every position The model outputs a probability distribution over the vocabulary at each position
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Step 3: Cross-entropy loss Compare predictions with actual next tokens. Penalize wrong predictions.
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Step 4: Backpropagate + update weights Gradients flow through attention and feed-forward layers. All positions train in parallel!
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🟡 Checkpoint Knowledge Check
During training, a Transformer processes a sentence of 1000 tokens. How many next-token predictions does it make in ONE forward pass?
💡 Think about causal masking — each position sees all previous tokens. What does each position predict?
The Transformer makes a prediction at EVERY position in parallel. Position 1 predicts token 2, position 2 predicts token 3, ..., position 999 predicts token 1000. That's 999 training signals from a single sequence in one forward pass — massively efficient compared to generating tokens one-by-one.
Impact
The Transformer Changed Everything
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🔴 Challenge Knowledge Check
What is the key advantage of self-attention over RNNs for capturing long-range dependencies?
💡 How many 'hops' does information need to travel between distant tokens in each architecture?
In an RNN, information from token 1 must pass through tokens 2, 3, 4, ..., n to reach token n — an O(n) path where signal degrades. In self-attention, every token directly attends to every other token — the path length is O(1). This makes it dramatically better at capturing long-range dependencies like 'The cat that sat on the mat in the house that Jack built' → 'cat...built'.
🎓 What You Now Know
✓ RNNs were sequential and slow — Processing one token at a time with vanishing gradients made them terrible at long sequences.
✓ Self-attention processes all tokens in parallel — Q, K, V vectors let every word attend to every other word simultaneously.
✓ Multi-head attention captures diverse patterns — Multiple attention heads learn syntax, semantics, coreference, and more independently.
✓ The architecture is elegantly simple — Attention + Feed-Forward + Residuals + LayerNorm, stacked N times. That’s it.
✓ Transformers conquered everything — Language, vision, audio, biology, robotics — one architecture to rule them all.
Check your quiz score → How many did you nail? 🎯
📄 Read the original paper: Attention Is All You Need (Vaswani et al., 2017)
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