scikit-learn
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
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136 skills found
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
Standardized Java development guidelines including naming conventions, exception handling, Spring Boot best practices, and concurrency patterns.
Multi-model LLM integration patterns for Claude, GPT, Gemini, and Ollama. Features API handling, prompt engineering, token management, and model-agnostic orchestration.
Train and manage neural networks in distributed E2B sandboxes using the Flow Nexus platform, supporting custom architectures like Transformers, LSTMs, and GANs.
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Local text-to-speech conversion using Kokoro TTS. Generate audio, read text aloud, and handle multilingual speech synthesis directly in your terminal.
Guide for implementing features using architecture-first design, TDD, rich domain models, and Swift 6.2 patterns, ensuring a clean separation between Domain, Infrastructure, and App layers.
A collection of design patterns for the Langroid multi-agent framework, covering agent configuration, tool handling, task orchestration, and external integrations.
Pragmatic AI-assisted coding standards focused on clean code, simplicity, and maintainability. Enforces best practices like SRP, DRY, and KISS to prevent over-engineering.
TypeScript development standards for LobeHub, covering type safety, async patterns, import organization, UI component integration, and performance optimization.
Create publication-quality plots and visualizations using matplotlib and seaborn. Works locally with any LLM.
Robot perception system design, configuration, and optimization for cameras, LiDAR, and sensor fusion pipelines. Includes camera calibration, 3D reconstruction, and production deployment best practices.