「orf」の検索結果

Use DeepDanbooru for anime-style image tag estimation, project and Danbooru-style dataset preparation, TensorFlow training, image evaluation, TFLite conversion, and experimental Grad-CAM workflows.

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Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.

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。

Guides CUDA-based BigGAN-PyTorch training, checkpoint sampling, dataset preparation, metric preparation, model customization, and optional TensorFlow Hub weight conversion.

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 Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

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。

Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

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.

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Think and work like an expert Computational Linguist. Use when a task calls for Computational Linguist judgment. Reasons from UD/PTB formalisms, validate.py/eval.py (LAS/MLAS/ELAS), and evalb .prm settings through Stanza/UDPipe pipelines, PropBank/FrameNet/AMR/UMR layers, IAA (κ, Krippendorff α), CONDA contamination checks, and ARR reproducibility while treating tokenizer mismatch, oracle inflation, train–test leakage, and guideline drift 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。

Workflow for ribosome profiling, P-site aware preprocessing, periodicity checks, ORF detection, and translation efficiency analysis.

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。

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Miro presents itself as the AI-powered visual workspace through a confident, almost playful brand voice — anchored by its signature canary yellow ({colors.brand-yellow}) wordmark over white canvas, broken open by colorful pastel feature tints (rose, teal, coral, orange, mint) that echo the actual sticky-note color palette used on the live whiteboard. Black-pill primary buttons dominate marketing, real Miro-board mockups serve as feature illustrations, and a 4-tier pricing grid leads into a dense comparison table. Roobert PRO carries display headlines; the system supports homepage, pricing, AI Workflows product page, agile vertical, and customer stories surfaces.

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Guide taxpayers through filing Geneva (Switzerland) tax returns with GeTax. Covers salary, professional expenses (forfaitaire vs effectifs), pillar 3a/3b, health insurance, medical expenses, securities (DA-1/RSI), foreign property, bank accounts, debts, crypto, and dual-income couples. Use when filing via GeTax, optimizing ICC/IFD deductions, declaring foreign property or income, handling DA-1 reclaims, or navigating Geneva cantonal tax rules.

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Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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。

Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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。

AI and ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML systems

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。

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

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Create, revise, render, or visually audit Quiet Signal A4 long-form PDF reports from source material using editable HTML/CSS, source-backed content, and full-page rendering QA. Use for detailed reports; not for slides or Xiaohongshu summaries.

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Turn Chinese text, articles, URLs, or user-defined page plans into polished 1080×1440 Xiaohongshu image-card series in the Quiet Signal visual language. Use when creating, revising, rendering, or visually auditing 小红书图文; use quiet-signal-design alone for non-Xiaohongshu design work.

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Apply or audit the approved Quiet Signal core visual language when the user explicitly asks for Quiet Signal styling on a defined website, interface, poster, social graphic, report, presentation, or other design artifact. This skill supplies visual identity and rule checks; it does not plan content, invent platform requirements, or run a production pipeline.

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Create professional Marp presentation slides with 7 beautiful themes (default, minimal, colorful, dark, gradient, tech, business). Use when users request slide creation, presentations, or Marp documents. Supports custom themes, image layouts, and "make it look good" requests with automatic quality improvements.

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Produces litigation-grade case summaries for animal law disputes including cruelty/neglect prosecutions, ownership/custody, dangerous animal designations, veterinary malpractice, and service/assistance animal accommodations. Builds exhibit-cited factual timelines, maps evidence to statutory elements, evaluates expert evidence, and assesses remedies with jurisdiction-specific legal research. Use when summarizing animal law cases, analyzing animal cruelty charges, pet custody disputes, dangerous dog hearings, veterinary malpractice claims, or assistance animal accommodation matters. Also trigger for animal forfeiture, bite liability, wildlife possession, or animal-related court filings.

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Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

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Use during or right after an incident — frames severity, sets comms cadence, drafts the post-mortem skeleton — even when the user just says 'production is down' or 'wir haben einen Vorfall'.

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Visualize métricas de treinamento, depure modelos com histogramas, compare experimentos, visualize grafos de modelos e perfil de desempenho com TensorBoard - kit de visualização de ML do Google

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Rastreie experimentos de ML, gerencie registro de modelos com versionamento, implante modelos em produção e reproduza experimentos com MLflow - plataforma agnóstica a frameworks para ciclo de vida de ML

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。