Algorithm deep-dives, engineering guides, and practical tutorials. New posts weekly.
A practical decision framework for choosing between the two most popular ensemble methods, with benchmarks on real datasets.
LLM & Modern AINo frameworks, no magic. Understand every component of Retrieval-Augmented Generation by building it yourself.
Deep LearningInteractive visualizations showing how different optimizers navigate loss landscapes, and why Adam usually wins.
DeploymentINT8, INT4, GPTQ, AWQ, GGUF — cut through the noise and learn which quantization method fits your use case.
TransformersSelf-attention, cross-attention, multi-head, flash attention — implement each from scratch and understand the math.
Best PracticesCommon pitfalls when choosing ML algorithms, from overfitting with neural nets on small data to ignoring feature engineering.