Writing about ML, data, and things I learn along the way.
A build-journey walkthrough of my Activity Recognition project using WISDM sensor data. Covers ingestion, phone accel/gyro fusion, sliding-window framing, XGBoost training, and what confusion matrix errors taught me.
Patterns and techniques for building RAG systems with LangGraph, Qdrant, and OpenAI. Covers chunking, hybrid retrieval, query rewriting, and hallucination prevention.
Building a bipartite genre network from the Steam database, projecting it into a weighted graph, running centrality and community detection, and learning why static game properties cannot predict playtime.
Implementing every ML algorithm twice: first from scratch in NumPy, then in PyTorch. From linear regression through CNNs, RNNs, and transformers, with miniprojects on MNIST, CIFAR-10, CelebA, and IMDB.
Techniques for extracting quantitative voice metrics from raw WAV files. Covers pitch tracking, loudness measurement, spectral analysis, and silence detection using Python libraries.