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deep-learning
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
Skills IA tools machine learning
Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools machine learning
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Explorer
GitHub
clustering
Discover patterns in unlabeled data using clustering, dimensionality reduction, and anomaly detection
GitHub
ml-fundamentals
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
GitHub
supervised-learning
Build production-ready classification and regression models with hyperparameter tuning
GitHub
computer-vision
Build computer vision solutions - image classification, object detection, and transfer learning
GitHub
ml-deployment
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
GitHub
data-ai
Linux data science and AI/ML environment setup - TensorFlow, PyTorch, CUDA, Jupyter
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.
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...
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...
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...
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...
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...
GitHub
rust-candle-core
Build native Rust ML models with Candle framework. Use when implementing vision transformers, LLMs, or audio models with GPU acceleration.
GitHub
rust-candle-whisper
Implement native Rust ML inference with Candle framework. Use when building GPU-accelerated ML pipelines without Python dependencies.
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...
GitHub
advanced-math-tradingsignal-processing-features
Signal processing, filtering/denoising, and math-driven feature engineering.
GitHub
advanced-math-tradingrobustness-risk
Tail risk, EVT, regularization, validation guardrails, and common pitfalls.
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.
GitHub
perform-sweep
Design, configure, launch, and analyze ablation sweeps for GRPO training. Use for hypothesis testing, hyperparameter experiments, and systematic comparisons.
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,...
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...
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.
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.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.