Unpack self-attention, multi-head routing, positional encoding, and the architecture behind modern AI.
Discover how Transformers broke the slow word-by-word bottleneck of older neural networks—using parallel processing and Self-Attention to power ChatGPT, Vision Transformers, and Multimodal AI.
How modern Transformers chop human language into LEGO bricks—from Byte-Pair Encoding (BPE) and WordPiece to Byte-Level Tokenization in GPT-4 and Llama-3.
Learn why Transformers are blind to word order without help—and how Sinusoidal Positional Encodings, Learned Embeddings, and RoPE (Rotary Position Embeddings) stamp every word with its place in line.
Learn how Self-Attention lets words talk directly to each other across a sentence—from the YouTube Search analogy for Query, Key, and Value (Q, K, V) to Scaled Dot-Product Attention and Causal Masks.
Learn how Multi-Head Attention gives a Transformer multiple pairs of eyes at once—from the Detective Team analogy and tensor head-splitting to Grouped-Query Attention (GQA) in Llama-3.
Learn how the three Transformer families work—comparing Bidirectional Encoders (BERT), Causal Decoders (GPT-4 & Llama-3), and Encoder-Decoders (T5 & Whisper) with Cross-Attention and Causal Masking.
A complete theoretical breakdown of the landmark 2017 'Attention Is All You Need' architecture—exploring the 6-layer Encoder stack, the 3-stage Decoder stack, Cross-Attention bridges, Residual Highways, LayerNorm, and Feed-Forward Networks.
