Understanding and Implementing Brant’s Tests in Ordinal Logistic Regression with Python. This article details the proportional odds model for ordinal logistic regression, its assumptions, and methods to assess the proportional odds assumption using likelihood ratio tests and separate fits approaches, with Python implementation examples.
A step-by-step guide to develop a custom code-to-diagram MCP server, explaining the fundamentals of Model Context Protocol and its components with a practical example.
Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here's how.
Rensa is a high-performance MinHash suite written in Rust with Python bindings. It's designed for efficient similarity estimation and deduplication of large datasets. It offers R-MinHash, C-MinHash, and OptDensMinHash variants, significantly faster than datasketch while maintaining comparable accuracy.
This article demonstrates how to use the attention mechanism in a time series classification framework, specifically for classifying normal sine waves versus 'modified' (flattened) sine waves. It details the data generation, model implementation (using a bidirectional LSTM with attention), and results, achieving high accuracy.
Apache Spark 4.0 marks a major milestone with advancements in SQL language enhancements, Spark Connect, reliability, Python capabilities, and structured streaming. It's designed to be more powerful, ANSI-compliant, and user-friendly while maintaining compatibility.
This is a GitHub repository for a Reinforcement Learning Tic Tac Toe project. It contains a single Python file, TicTacToeRL.py. The repository has 0 stars and 0 forks as of the current data.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
This practical guide uses SERP comparisons and Python to group keywords by intent, faster and more intuitively.
