Transformer Architecture Explained: Encoder, Decoder, and Encoder-Decoder (BERT, GPT, T5)
How does the transformer architecture actually work? Discover the key differences between BERT, GPT, and T5, and learn when to use encoders vs decoders.
We break down the core neural network design powering modern generative artificial intelligence. You will learn the mechanics behind bidirectional encoders, causal decoders, and sequence-to-sequence models. We walk through the mathematical calculations of query, key, and value vectors, showing how attention masking controls information routing. By understanding how these layers process inputs, you can build and configure large language models more effectively.
✦ How does scaled dot-product attention compute token relationships?
✦ Why do decoder-only models require a causal mask during text generation?
✦ When should you choose encoder-decoder models like T5 or BART over GPT?
✦ How does cross-attention connect the encoder and decoder stacks?
This deep-dive guide is based on the foundational papers of Vaswani et al. (2017), Devlin et al. (2018), and Raffel et al. (2019). We cross-reference the math, parameters, and training objectives of BERT, GPT-2, and T5 to give you a rigorous, mathematically accurate comparative breakdown.
Which transformer family do you use most in your projects? Let us know in the comments below!
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