InsideEdgeInsideEdge
Download App

Phase 8 · Deep Learning Architectures

Topics

Transformer Architecture

Part of the AI Engineer Roadmap.

Summary

The architecture built entirely around self-attention (no recurrence) that underlies GPT, BERT, Claude and virtually every modern LLM.

How to Learn This

  • 1Study the Transformer diagram piece by piece: embeddings, attention, feed-forward, layer norm.
  • 2Implement a minimal Transformer block from scratch (or follow a guided walkthrough like 'nanoGPT').
  • 3Learn the difference between encoder-only (BERT), decoder-only (GPT) and encoder-decoder (T5) Transformers.

Previous

Attention Mechanism

Next

Positional Encoding

More topics in Deep Learning Architectures

Convolutional Neural Networks (CNNs)Pooling LayersRecurrent Neural Networks (RNNs)LSTMs & GRUsSequence-to-Sequence ModelsAttention MechanismPositional EncodingTransfer LearningAutoencodersGenerative Adversarial Networks (GANs, Overview)
InsideEdge

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download
InsideEdge