A series of study notes on deep anomaly detection, covering its applications, the aspects that guide model choice, and model categories by label availability. Each post follows a structured outline covering assumptions, architectures, computational complexity, and pros and cons.
Website: https://dsworld.org/
A personal technical newsletter from an engineer working through applied machine learning, covering model interpretability, feature engineering and cost-based decision analysis for manufacturing problems. Issues include study notes with glossaries and a worked technical memo with concrete figures and recommendations.
Long-form essays on evaluating and specifying AI systems, drawing on hands-on projects such as a UFC judging model and an evaluation of a new model launch. Readers get practical lessons on labeling, ground truth, build-versus-buy decisions and precise AI terminology.
Tutorials and analysis on college football ratings, using CollegeFootballData data and Python. Readers get walkthroughs of opponent adjustment, rating methodology and comparisons of team-strength measures with code and real 2025 season examples.
A multi-issue weekly newsletter covering machine learning and AI, including weekly industry roundups, editorials, and deep dives into research topics such as recursive self-improvement. Readers get explanations of new models, research papers, and the business dynamics of the AI industry.
One short email a week with hand-picked newsletters from the index. Free, no spam, unsubscribe anytime.
