Transformers Explained: How AI Understands Context

How does AI understand the relationship between words and context? In this AICloud explainer, we break down Transformer architecture from the ground up — from tokens and embeddings to positional information, self-attention, feed-forward networks, residual connections, and stacked Transformer blocks. You'll also see how the original Encoder–Decoder Transformer connects to modern decoder-only models like GPT, and why Transformers became foundational to language, code, vision, and multimodal AI. Topics covered: • What is a Transformer? • Tokenization & embeddings • Positional information • Multi-Head Self-Attention • Feed-Forward Networks • Residual Connections & Normalization • Encoder vs Decoder • GPT & next-token prediction • Transformer applications AICloud — making AI concepts easier to understand. #ai #artificialintelligence #transformers #machinelearning #deeplearning #generativeai #LLM #chatgpt #selfattention #aiexplained #aicloud #claude