「tuning」の検索結果

Comprehensive best practices, design patterns, and common pitfalls for ROS2 (Robot Operating System 2) development. Use this skill when building ROS2 nodes, packages, launch files, components, or debugging ROS2 systems. Trigger whenever the user mentions ROS2, colcon, rclpy, rclcpp, DDS, QoS, lifecycle nodes, managed nodes, ROS2 launch, ROS2 parameters, ROS2 actions, nav2, MoveIt2, micro-ROS, or any ROS2-era robotics middleware. Also trigger for ROS2 workspace setup, DDS tuning, intra-process communication, ROS2 security, or deploying ROS2 in production. Also trigger for colcon build issues, ament_cmake, ament_python, CMakeLists.txt for ROS2, package.xml dependencies, rosdep, workspace overlays, custom message generation, or ROS2 build troubleshooting. Covers Humble, Iron, Jazzy, and Rolling distributions.

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。

Provides AWS SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK usage.

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。

Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.

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Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.

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。

Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.

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。

Use FlagEmbedding for embedding, reranking, retrieval evaluation, and fine-tuning workflows.

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。

Use Flair for NLP annotation, embeddings, text/sequence model prediction, corpus loading, training/fine-tuning, HunFlair biomedical NER/linking, and production troubleshooting.

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。

Use Donut for OCR-free document understanding, checkpoint inference, fine-tuning, evaluation, and SynthDoG synthetic document generation.

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。

Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.

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。

Use Chronos Forecasting for pretrained time-series forecasting, Chronos-2 covariates, data validation, fine-tuning, evaluation, and deployment planning.

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。

Route Chinese-LLaMA-Alpaca model reconstruction, inference/deployment, training/fine-tuning, and evaluation workflows.

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。

Route Baichuan2 chat inference, deployment/quantization, and fine-tuning workflows.

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。

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

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning

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。

Implement privacy-preserving record linkage across datasets using Bloom filter encoding, secure hash matching, threshold tuning for precision and recall, and false positive management. Enables entity resolution without exposing raw personally identifiable information between parties.

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。

Guide to implementing homomorphic encryption for privacy-preserving computation under GDPR. Covers scheme selection (BFV, BGV, CKKS, TFHE), Microsoft SEAL, IBM HELib, and Google FHE transpiler. Includes performance benchmarks, parameter tuning, and basic HE example code for encrypted arithmetic operations.

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Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.

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。

Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.

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。

Create or audit SLOs, SLIs, alert rules, incident response steps, escalation paths, postmortems, operational runbooks, and customer-impact communication. Use when defining production reliability, preparing launch readiness, responding to an outage, writing a runbook, tuning alerts, or closing the loop after an incident.

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。

Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.

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。

Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.

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。

Think and work like an expert Mass Spectrometrist. Use when a task calls for Mass Spectrometrist judgment. Reasons from ion formation, m/z resolution and mass accuracy, fragmentation, and calibrated ion statistics through ESI/APCI/MALDI tuning, CID/HCD/ETD MS/MS, isotope-pattern formula assignment, and spectral libraries (NIST, mzCloud, GNPS) under FDA/ICH M10/MSI tiers, while treating matrix suppression, PEG/siloxane/keratin contamination, decoy-driven FDR inflation, and unassigned adducts as first-class failure modes.

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。

Think and work like an expert Climate Scientist. Use when a task calls for Climate Scientist judgment. Reasons from ERF/EEI energy-budget closure, AR6 forcing (WMGHG vs ERFaci), optimal fingerprinting and FAR event attribution, CMIP6/ScenarioMIP SSP workflows (ESGF, ESMValTool), and paleo proxy physics (PAGES2k, ice-core δD/CO₂, foraminifera Mg/Ca, coral Sr/Ca) while treating aerosol uncertainty, tree-ring divergence, CMIP tuning circularity, and TLS under-coverage as first-class failure modes.

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。