「model-training」の検索結果

Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.

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。

Agent-driven YOLO fine-tuning — annotate, train, export, deploy

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。

Analyze and apply AssemblyAI secret, region, consent, retention, deletion, and model-training controls. Use when reviewing security or regulated speech workflows. Trigger with "AssemblyAI security", "AssemblyAI privacy", or "data retention".

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 generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune a LoRA, clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, LoRA, dataset curation, epoch previews.

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。

Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.

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。

基于ark_sdk的大模型训练任务自动化工具。帮助用户通过自然语言创建并提交方舟模型训练任务，支持用户自备数据的SFT训练、RFT+GRPO和直接GRPO策略，并引导用户完成训练、状态跟踪与评估闭环。使用场景：当用户需要进行大模型SFT监督微调、RLHF训练、GRPO训练、RFT训练，或需要自动化训练流程时触发。

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。

Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

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。

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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 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

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。

Design a hyperparameter tuning strategy for a model — search space, method, and budget. Use when asked to "tune hyperparameters", "define a search space", or "set a tuning budget".

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。

Audit existing model training code — find reproducibility issues, data leakage, and missing best practices. Use when asked to "audit our training code", "is our training reproducible", or "check for training data leakage".

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。

Design a model training pipeline — algorithm selection, cross-validation, and serialization. Use when asked to "train a model for this", "design a training pipeline", or "which algorithm should we use".

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。