Feed-Forward Network — 2 Technical Articles

Feb 14, 2026 · Artificial Intelligence

Unpacking the Transformer: From Embeddings to Multi‑Head Attention

This article provides a comprehensive, step‑by‑step walkthrough of the Transformer architecture, covering input embedding, positional encoding, the mechanics of Q‑K‑V attention, scaled dot‑product formulas, multi‑head and masked attention, feed‑forward networks, residual connections, layer normalization, decoder generation, and recent attention‑optimization techniques.

AttentionFeed-Forward NetworkMulti-Head Attention

0 likes · 39 min read

Unpacking the Transformer: From Embeddings to Multi‑Head Attention

Architect

Mar 19, 2024 · Artificial Intelligence

How Transformers Power Modern NLP: A Deep Dive into Encoder‑Decoder Mechanics

This article explains the core principles of Transformer models—covering input embeddings, self‑attention, multi‑head attention, positional encoding, feed‑forward networks, and decoder strategies—using concrete examples like "The cat sat on the mat" and "The quick brown fox jumps over the lazy dog" to illustrate each step.

Encoder-DecoderFeed-Forward NetworkMulti-Head Attention

0 likes · 13 min read

How Transformers Power Modern NLP: A Deep Dive into Encoder‑Decoder Mechanics