02 / Data
Data
Learning to build and lead AI in the real world — transformers, fine-tuning, and enterprise software, explained from first principles.
Series
Reading tracks
Structured series — read top to bottom, or dip into a single piece.
Series 01 · 🔒 Private6 lessons
Foundations
Core mathematics and concepts behind modern ML — neural-net fundamentals, the softmax + cross-entropy stack, statistical inference. Each note stands alone; together they form the shared language of everything else in Data.
View the series →Series 02 · 🔒 Private1 lesson
Hardware & Compute
Where the maths meets the silicon. GPUs, CUDA, Apple's MPS/Metal/MLX stack, and what runs locally vs. needs a rented cloud GPU.
View the series →Series 03 · 🔒 Private5 lessons
Transformer Architectures
From the 2017 paper to today's LLMs. Self-attention and the QKV trio, the GPT decoder-only branch, RLHF and alignment, and the modern transformer anatomy you'd actually fine-tune.
View the series →