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Published directory entries matching your search.
Cerberus is a lightweight and extensible data validation library for Python. It provides type checking and other base functionality out of the box, is designed to be non-blocking and extensible for custom validation, and has no dependencies. It is available on PyPI and maintained according to semantic versioning since version 1.2.
AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in 10 minutes than regret in six months. Powered by Claude Code, OpenAI Codex, and Cursor.
AI Models Catalog is a structured YAML catalog of AI model providers and their models, sourced from first-party APIs and official documentation. It includes TypeScript type definitions and Zod runtime validation schemas, plus scripts to sync data from providers and validate YAML files against schemas.
Blocklist is a privacy DNS service at tiar.app offering DNS resolution over DNS, DNSCrypt, DoH/DoH3, DoQ and DoT. It filters ads, ad-tracking and malware, and states DNSSEC validation, no EDNS Client Subnet and no logs. It is published on GitHub under an MIT license.
Deepchecks is an open-source project for validating machine learning models and data from research to production. It includes built-in and custom checks and suites for tabular, NLP and CV validation, CI and testing management, and monitoring of deployed models. Installation is via pip.
Eurybia is an open-source tool hosted on GitHub under the MAIF organization. It is designed to monitor model drift over time and secure model deployment processes through data validation functionality, with public documentation covering overview, quickstart, installation, features, and project background.
EvalML is an AutoML library written in Python that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. It includes data quality checks, cross-validation, preprocessing, feature engineering, feature selection, and model understanding tools. Install from PyPI or conda-forge.
An archived, read-only GitHub repository holding an open solution to the Home Credit Default Risk challenge. It provides code, configs and notebooks plus documented solutions using LightGBM, sklearn and XGBoost with engineered features; the README lists cross-validation and leaderboard scores for each. References to Neptune are optional and the solution can run as a plain Python script.
pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models. It implements data structures for models such as DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks and Structural Equation Models, with algorithms for tasks including causal discovery, causal identification, inference, model validation, parameter estimation and simulations. Algorithms follow a unified composable API and are scikit-learn compatible when possible. It is available on PyPI and conda.
prompt-to-asset is a GitHub repository containing an MCP server and command-line interface tool for generating production-grade software assets including logos, app icons, favicons, Open Graph images and illustrations. The tool routes requests across more than 30 image models, supports zero-key initial usage, and includes validation functionality to review assets before they are shipped.
Uploadcare Widget is an HTML5 file upload tool that supports multiple file uploads, drag and drop functionality, validation by file size, file extension, and MIME type, upload progress bars, and image previews. A deprecation notice for the jQuery File Uploader component is included in the repository. It is hosted as a public repository on GitHub.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection. The repository includes training, validation, inference, and multi-format export tooling, and reuses shared utilities from the ultralytics package. Detection is framed as a single regression problem predicting bounding boxes and class probabilities from full images in one forward pass.
