Transformers are a revolutionary neural network architecture that has transformed AI. Unlike RNNs that process sequences one step at a time, Transformers process all positions simultaneously using self-attention mechanisms. They can weigh the importance of different parts of the input when making predictions. Transformers power GPT, BERT, ChatGPT, and most modern large language models. They've become the foundation of state-of-the-art NLP, and increasingly, computer vision and other domains.
Watch how words attend to each other in a sentence
Each word looks at all other words to understand context
The breakthrough of Transformers is self-attention. When processing the word "it" in the sentence "The animal didn't cross the street because it was too tired", the network can attend more to "animal" than "street" to understand what "it" refers to. Unlike RNNs that forget distant words, Transformers can directly connect any two words, regardless of distance. This allows them to capture long-range dependencies effortlessly and process sequences in parallel, making them much faster to train than RNNs.
Translating "The bank can guarantee deposits will eventually cover future tuition costs" to French:
Input: English sentence tokens
Positional Encoding: Add position information to each word
Self-Attention: "bank" attends to "deposits" and "guarantee" (financial context)
Multi-Head Attention: Different heads capture different relationships
Encoder Output: Rich contextual representation of English sentence
Decoder Attention: French words attend to relevant English words
Output: "La banque peut garantir..." (accurate translation preserving meaning)
1. Input Embeddings: Convert words to vectors + positional encoding
2. Encoder Stack (6-12 layers):
ā¢ Multi-head self-attention (words attend to all words)
ā¢ Add & Normalize
ā¢ Feed-forward network
ā¢ Add & Normalize
3. Decoder Stack (6-12 layers):
ā¢ Masked self-attention (attend to previous words only)
ā¢ Cross-attention to encoder output
ā¢ Feed-forward network
4. Output: Softmax over vocabulary to predict next word
Sequential Processing: Must process one word at a time
Slow Training: Can't parallelize across time steps
Limited Context: Struggles with very long sequences
Vanishing Gradients: Hard to learn long-term dependencies
Parallel Processing: Process all words simultaneously
Fast Training: Highly parallelizable on GPUs
Global Context: Direct connections to all positions
Stable Gradients: Attention provides gradient highways
Decoder-only architecture. Trained to predict next word. Powers ChatGPT, GPT-4. Excels at text generation and few-shot learning.
Encoder-only architecture. Trained with masked language modeling. Great for understanding and classification tasks.
Full encoder-decoder. Treats all tasks as text-to-text. Unified framework for translation, summarization, question answering.
Applies Transformers to images by treating patches as tokens. Outperforms CNNs on many vision tasks when trained with sufficient data.