「training」の検索結果

Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.

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Use when polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM algorithm internships from raw resume text, a materials folder, and/or a target job description. Audits evidence, maps JD fit, enforces truth boundaries, writes polished and targeted resumes, generates interviewer-style grilling questions, answer cards, evidence-upgrade plans, and optional open-source project recommendations without fabricating experience.

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Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.

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Implement the NIST Privacy Framework GOVERN function covering GV.AT awareness and training, GV.MT monitoring and review, GV.PO policy development, and GV.RR roles and responsibilities. Provides governance structure templates, training programs, and accountability frameworks for privacy governance.

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Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.

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Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

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Conducts structured post-breach remediation using a lessons learned framework covering root cause remediation, control gap closure, policy updates, training modifications, monitoring enhancements, and regulatory follow-up. Provides a systematic approach to preventing breach recurrence and demonstrating accountability to supervisory authorities. Keywords: post-breach, remediation, lessons learned, root cause, control gap, policy update, training.

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Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.

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TRIGGER THIS when building DEI strategy, creating diversity goals, designing inclusive programs, building equity analyses, developing training, addressing inclusion issues, or tracking DEI metrics. Develops comprehensive diversity, equity, and inclusion strategies aligned to business goals with measurable outcomes, training programs, metrics frameworks, and accountability systems.

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Use when user asks to clone a voice, train a custom voice model, or synthesize speech with a cloned voice. iFlytek Voice Clone tts(声音复刻) — train a custom voice model from audio samples and synthesize speech with the cloned voice. Supports the full workflow: get training text → create task → upload audio → submit training → poll results → synthesize with cloned voice. Pure Python stdlib, no pip dependencies.

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Use when training, tuning, comparing or evaluating a model — choosing the cross-validation scheme, baselines, tuning inside CV, thresholds and calibration, per-slice metrics, feature importance and error analysis

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Builds a GPT and BPE tokenizer from scratch. Use when implementing autograd, attention, a nanoGPT loop, muP hyperparameter transfer, WSD annealing, loss spikes, or mid-training.

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Design a professional development session using adult learning principles with active teacher engagement. Use when planning INSET days, CPD workshops, or staff training sessions.

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Aural skills development covering interval recognition, chord quality identification, rhythmic and melodic dictation, sight-singing, and progressive pedagogy. Covers the Kodaly method (hand signs, relative solmization, singing-first approach), interval identification strategies, chord quality and inversion recognition, harmonic dictation, solfege systems (fixed-do vs. movable-do), Gordon's Music Learning Theory, Suzuki method principles, and structured difficulty sequencing. Use when developing listening skills, practicing dictation, preparing sight-singing, or designing ear training curricula.

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Somatic movement and whole-body retraining as Joseph Pilates designed it in the Contrology method and as Moshé Feldenkrais designed it in Awareness Through Movement and Functional Integration, with enough cross-reference to the broader somatics landscape (Alexander Technique, Hanna Somatics, Body-Mind Centering) that an agent can place a user into the right method. Covers the Pilates reformer and mat system, the six Pilates principles, the Feldenkrais ATM lesson structure, the nervous-system learning frame that distinguishes somatics from exercise, and the safety posture that matters for rehab populations. Use for queries about core training, rehab-adjacent movement, chronic pain patterns, and learning-based movement re-education.

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Martial arts as body discipline — the shared pedagogical structure across striking, grappling, and weapon arts (stance, distance, timing, ukemi, live partner work, graded ranking, and the ethical container the best schools enforce). Covers karate, judo, jiu-jitsu (Brazilian and traditional Japanese), aikido, kendo/kenjutsu, boxing, muay Thai, and wrestling at enough depth that an agent can place a user into a reasonable first-month practice and recognize the safety failures that matter most. Leans on Moshé Feldenkrais's judo background and Yang Jwing-Ming's martial scholarship for the internal-to-external bridge. Use for any query about beginning a martial art, training injury patterns, graded progression, or the ethics of combat training.

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AgentDB Reinforcement Learning Training operates on 3 fundamental principles:

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Run Claude Code, Codex CLI, Gemini CLI, or OpenCode through bounded H100 post-training tasks and compare how well each agent improves a base LLM.

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Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.

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Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.

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应用从 AI for Everyone 蒸馏出的「分层AI培训 — 高管/经理/工程师的差异化学习」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to role-tiered-training.

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Adapt torch_npu.profiler interface for AI frameworks and training/inference code on Ascend NPU. Use this skill whenever users need to integrate profiling, add performance data collection, or instrument PyTorch code with torch_npu.profiler on Huawei Ascend NPU platforms. Triggers on: "torch_npu profiler", "NPU profiling", "Ascend profiler", "profiler adaptation", "profile training", "profile inference", "add profiler to framework", "性能采集", "profiling 数据", "昇腾 profiler", and any request to add performance monitoring to Ascend NPU code.

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Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.

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Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

日本語の概要は準備中です。原文の説明を表示しています。