GitHub
deep-learning
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
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tools machine learning AI 技能
发现 tools machine learning 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
分类目录: tools machine learning
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GitHub
clustering
Discover patterns in unlabeled data using clustering, dimensionality reduction, and anomaly detection
☆ 1查看技能
GitHub
ml-fundamentals
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
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GitHub
supervised-learning
Build production-ready classification and regression models with hyperparameter tuning
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GitHub
computer-vision
Build computer vision solutions - image classification, object detection, and transfer learning
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GitHub
ml-deployment
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
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GitHub
data-ai
Linux data science and AI/ML environment setup - TensorFlow, PyTorch, CUDA, Jupyter
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GitHub
data-ai-guide
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
☆ 1查看技能
GitHub
domino-jobs
Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers job configuration, hardware tiers, scheduled jobs (cron), monitoring status, viewing logs, and API-drive...
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GitHub
domino-model-endpoints
Deploy and monitor model API endpoints in Domino. Covers creating prediction endpoints, version management, Grafana dashboards for latency/errors/resources, alerting, and GPU inference with NVIDIA Triton. Use w...
☆ 1查看技能
GitHub
domino-flows
Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Define DAGs with typed inputs/outputs, heterogeneous environments, automatic lineage, and reproducibility. Use when building data pipelin...
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GitHub
domino-model-monitoring
Monitor deployed models in Domino including drift detection, model quality tracking, and alerting. Covers data drift analysis, prediction capture, baseline comparison, alert configuration, and remediation workf...
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GitHub
domino-experiment-tracking
Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and...
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GitHub
rust-candle-core
Build native Rust ML models with Candle framework. Use when implementing vision transformers, LLMs, or audio models with GPU acceleration.
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GitHub
rust-candle-whisper
Implement native Rust ML inference with Candle framework. Use when building GPU-accelerated ML pipelines without Python dependencies.
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GitHub
ml-reviewer
WHEN: Machine Learning/Deep Learning code review, PyTorch/TensorFlow patterns, Model training optimization, MLOps checks WHAT: Model architecture review + Training patterns + Data pipeline checks + GPU optimiza...
☆ 1查看技能
GitHub
advanced-math-tradingsignal-processing-features
Signal processing, filtering/denoising, and math-driven feature engineering.
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GitHub
advanced-math-tradingrobustness-risk
Tail risk, EVT, regularization, validation guardrails, and common pitfalls.
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GitHub
experiment-analysis
Analyze GRPO training runs for learning dynamics and pipeline performance. Use when diagnosing training issues, reviewing Elo progression, checking throughput, or updating experiment results.
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GitHub
perform-sweep
Design, configure, launch, and analyze ablation sweeps for GRPO training. Use for hypothesis testing, hyperparameter experiments, and systematic comparisons.
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GitHub
multi-model-validation
Run multiple AI models in parallel for 3-5x speedup with ENFORCED performance statistics tracking. Use when validating with Grok, Gemini, GPT-5, DeepSeek, or Claudish proxy for code review, consensus analysis,...
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GitHub
workers-ai
This skill should be used when the user asks about "Workers AI", "AI models", "text generation", "embeddings", "semantic search", "RAG", "Retrieval Augmented Generation", "AI inference", "LLaMA", "Llama", "bge...
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GitHub
proxy-mode-reference
Reference guide for using PROXY_MODE with external AI models. Use when running multi-model reviews, understanding which agents support PROXY_MODE, or debugging external model integration issues.
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GitHub
ml-engineer
Implement ML pipelines, model serving, and feature engineering. Handles TensorFlow/PyTorch deployment, A/B testing, and monitoring. Use PROACTIVELY for ML model integration or production deployment.
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。