Research books
Longer studies that needed more than a paper. Each book ships with a PDF; the web reader renders the same source so the two never drift.
Mathematical Awakening: Connecting the Equations of Nature and Intelligence
A long-form pass through the mathematics ML engineers actually use: calculus, linear algebra, probability, and statistics, with worked examples that connect classical physics to modern machine learning. The 2026 revision preserves the original Chapters 1-8 and refreshes Chapters 9-10 to cover rotary position embeddings (RoPE), grouped-query attention, flash attention, scaling laws, GRPO, and diffusion / flow matching, closing with a reading walkthrough of the DeepSeek-R1 paper. Python snippets are embedded throughout to visualise concepts and show how the math maps to code you'd recognise from PyTorch or NumPy.
Open bookApplied ML 2026: Reproducible Projects for the LLM Era
An in-progress companion volume to Mathematical Awakening, being written from scratch in the LLM era. Each project ships with a reproducible rig (experiments/run.py → metrics.json) in the tradition of the rest of the Aresalab papers: LLM-first patterns (retrieval, tool-use, evals, distillation, quantisation), domain depth (healthcare, bioinformatics, robotics), and honest failure modes. Published chapter-by-chapter as each project lands with a running experiment.
Open bookEarlier editions, kept on the shelf
Books that are no longer being updated. URLs stay live so citations keep resolving, and the PDFs are preserved as-is with clearly-dated front matter.