This article details how to build a lightweight and efficient rules engine by recasting propositional logic as sparse algebra. It guides readers through the process from theoretical foundations to practical implementation, introducing concepts like state vectors and algebraic operations for logical inference.
A step-by-step guide to catching real anomalies without drowning in false alerts.
This tutorial compares Polars and pandas, covering syntax, performance, LazyFrames, conversions, and plotting to help you choose the right library for your data analysis needs.
This article explores how prompt engineering can be used to improve time-series analysis with Large Language Models (LLMs), covering core strategies, preprocessing, anomaly detection, and feature engineering. It provides practical prompts and examples for various tasks.
The author discusses a shift in approach to clustering mixed data, advocating for starting with the simpler Gower distance metric before resorting to more complex embedding techniques like UMAP. They introduce 'Gower Express', an optimized and accelerated implementation of Gower.
This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
This article explores the impact of hyperparameters on random forests, both in terms of performance and visual representation. It compares the performance of a default random forest with tuned decision trees and examines the effects of various hyperparameters like `n_estimators`, `max_depth`, and `ccp_alpha` using visualizations of individual trees, predictions, and errors.
Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs. This article explores Google’s LangExtract framework and its open-source LLM, Gemma 3, demonstrating how to parse an insurance policy to surface details like exclusions.
Learn how to connect several essential tools to develop a simple yet intuitive dashboard using Streamlit, Plotly, DuckDB, and Pandas to visualize data from a JSON file.
This article explores alternatives to NotebookLM, a Google assistant for synthesizing information from documents. It details NousWise, ElevenLabs, NoteGPT, Notion, Evernote, and Obsidian, outlining their key features, limitations, and considerations for choosing the right tool.
