Search Results for "reasoning machine learning"

Collection of 111 standalone TikZ figures for illustrating concepts in physics, chemistry, and machine learning. Check out janosh.github.io to search, sort, open in Overleaf, and download figures (PDF/SVG/PNG) from this collection.

pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,...

unfluff is a Node.js library designed to automatically extract the main content from an HTML document — stripping away navigation bars, ads, footers and other boilerplate to leave you with the “body content”, metadata (title, author, date) and other useful fields. It’s a tool very much aimed at content-analysis, web scraping, building datasets, or repurposing article text for downstream processing (like machine-learning or summarization). The API is simple: you feed in raw HTML and it returns a structured object with the extracted text and other fields. It supports caching internal representations to speed up repeated extractions. While its language support is best for English, it is still widely used in web-content-processing pipelines. ...

This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made.

LPCforSOS is a machine learning framework with a special focus on structured output spaces and pairwise learning. It supports currently multiclass, ordinal, hierarchical, multi-label and label ranking classification settings.

The aim of ALIVE is to develop new approaches to the engineering of flexible, adaptable distributed service-oriented systems based on the adaptation of social coordination and organisation mechanisms.

SAIM allows to interlink knowledge bases in the Semantic Web. It focuses on instance matching of very large knowledge bases available as SPARQL endpoints. SAIM uses machine learning techniques and is compatible with SILK.

Blunder is an automated tool for analyzing chained exceptions in Java. It's usefull for classify, generate a customized error message and a list for possible solutions.