machine learning

From 400,000 Natural Products to 20 Candidates

A funnel from every catalogued natural product down to twenty compounds worth testing, and why the split and the base rate matter more than the model.

Machine Learning for Biology05

Your Negatives Decide Your Accuracy: 92% That Means Nothing

Random UniProt negatives quietly turn peptide prediction into short versus long. Here is what an honest benchmark costs, and why it is worth paying.

Machine Learning for Biology04

What ESM-2 Learned That Letter-Counting Couldn't

A 650-million-parameter protein language model, what its embeddings really encode, and why twenty lines of letter counting still beat it on 13-residue peptides.

Machine Learning for Biology03

Turning a Peptide Into Numbers: A Tour of the Descriptor Zoo

AAC, DPC, DDE, CKSAAP, CTD, PAAC and QSO: every way I know to turn a peptide into a fixed-length vector, and what each one throws away.

Machine Learning for Biology02

Peptides 101: The Tiny Molecular Sentences Your Body Writes

What a peptide actually is, why order and side chains carry the information, and why 13 residues is a different modelling problem from 300.

Machine Learning for Biology01

The Slowest Kid Problem: How a Super Captain Solves MoE's Biggest Headache

Why a mixture-of-experts layer runs at the speed of its busiest expert, how to measure it, and when predictive prefetching actually helps.

Mixture of Experts02

LexSubLM-Lite: Lightweight Lexical Substitution That Runs Anywhere

A laptop-sized toolkit for context-aware lexical substitution, and an honest look at what tiny models actually score on it.

From One Brain to Many: Understanding Mixture of Experts (MoE) Like You're 12

How sparse mixture-of-experts really works: per-token routing, top-k gating, load balancing, and the memory bill you pay for the compute you save.

Mixture of Experts01

Machine Learning 101: PyTorch, TensorFlow & Decision Trees

What a deep-learning framework actually does for you, how a decision tree picks its questions, and which of the three to reach for on real data.

AI Foundations02

Machine Learning Algorithms by Hand

Four algorithms in forty lines each, with no library. k-nearest neighbours, linear and logistic regression, k-means, and a decision tree, plus what each one assumes.

Problem Solving From Zero20

Homomorphic Encryption (HE) Explained: A Beginner’s Guide to Secure AI on Encrypted Data

What homomorphic encryption really does, which scheme to pick, what a ciphertext costs in bytes and depth, and where it still loses to a trusted enclave.