AI Knowledge Hub

Transformer Architecture

The revolutionary neural network architecture that powers modern AI
Key Innovation: Self-Attention

Self-attention allows each position in a sequence to attend to all positions in the same sequence. This enables the model to capture relationships between distant elements effectively.

Attention Formula:
Attention(Q,K,V) = softmax(QK^T/√d_k)V

Where Q = Queries, K = Keys, V = Values, d_k = dimension of keys

Architecture Components
Multi-Head Attention

Allows the model to attend to different parts of the sequence simultaneously

Uses multiple attention heads to capture different types of relationships in the data

Positional Encoding

Provides position information since transformers don't inherently understand sequence order

Uses sinusoidal functions to encode position information into the input embeddings

Feed-Forward Networks

Applies non-linear transformations to each position independently

Two linear transformations with ReLU activation in between

Layer Normalization

Normalizes inputs to each layer to stabilize training

Applied before each sub-layer (pre-norm) or after (post-norm)

Encoder-Decoder Structure

Encoder Stack

  • 6 Identical Layers: Each with multi-head attention and feed-forward network

  • Self-Attention: Looks at all positions in the input sequence

  • Residual Connections: Helps with gradient flow during training

  • Layer Normalization: Stabilizes training process

Processes entire input sequence in parallel
Decoder Stack

  • 6 Identical Layers: Similar to encoder but with masked attention

  • Masked Self-Attention: Prevents looking at future tokens

  • Encoder-Decoder Attention: Attends to encoder output

  • Autoregressive: Generates output one token at a time

Generates output sequentially

Why Transformers Work So Well

Parallelizable computation (unlike RNNs)

Better at capturing long-range dependencies

More efficient training on modern hardware

Foundation for state-of-the-art models

Excellent transfer learning capabilities

Transformer Variants
Encoder-Only Models

BERT, RoBERTa - Great for understanding tasks like classification

Decoder-Only Models

GPT series - Excellent for text generation and completion

Encoder-Decoder Models

T5, BART - Perfect for translation and summarization tasks

Training Considerations

  • Computational Requirements: High memory and compute needs

  • Data Requirements: Large amounts of training data needed

  • Optimization: Careful learning rate scheduling required

  • Regularization: Dropout and weight decay important