'training' 검색 결과

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

기계 학습이 없는 기존 앱에 추천, 분류, 예측 기능을 추가하는 절차를 정리합니다. 문제와 데이터 확인부터 초기 모델의 학습, 평가, 통합까지 돕습니다.

• 기존 앱에 추천 기능 추가
• 학습 데이터 준비 상태 확인
• 대체 규칙을 갖춘 예측 기능 통합

텍스트를 AI 모델이 다루는 작은 단위로 나누고 숫자 배열로 변환합니다. 대량 처리, 맞춤 어휘 학습, 원문 위치 대응과 입력 길이 조정을 돕습니다.

• 대량 텍스트를 모델 입력으로 전처리
• 텍스트 파일로 맞춤 어휘 학습
• 예측 결과를 원문 위치에 연결

NeMo Curator로 LLM 학습 데이터의 중복과 저품질 내용을 제거하고 개인정보를 삭제하거나 대체합니다. 처리 흐름 구성과 이미지, 영상, 음성 정리도 다룹니다.

• 웹 수집 문장을 학습용으로 정리합니다
• 완전 일치, 유사, 의미 기반 중복을 제거합니다
• 텍스트의 개인정보를 삭제하거나 대체합니다

PowerPoint 자료를 읽고 새로 만들거나 기존 슬라이드를 조정하도록 지원합니다. 경영진 설명 자료, 교육, 제품 검토에 쓰는 레이아웃과 템플릿도 다룹니다.

• 경영진을 위한 설명 자료 제작
• 교육용 슬라이드 준비
• 제품 검토 자료 제작

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

거래 전략의 장기 검증, 여러 조건 비교와 모델 학습을 클라우드에서 수행합니다. 비용 추정부터 성과 지표와 CSV 수집, 결과 서명 검증과 저장까지 지원합니다.

• 거래 전략의 여러 해 성과 검증
• 다양한 매개변수 조합 비교
• 클라우드에서 LSTM 등 모델 학습

Turn agent failure records into training examples by locating the first incorrect decision and pairing preferred actions with rejected ones. Assess whether training is appropriate, then check both targeted improvements and preservation of correct behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing the first incorrect decision in an agent failure
• Building preference pairs from failure records
• Choosing between system fixes and model training

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure training for an image-and-text task
• Decide whether to train vision layers
• Investigate training with flat evaluation scores

Prepare and review LoRA or QLoRA settings for adapting language models using example responses. Choose trainable layers, adapter capacity, learning rate, and memory-related options, then produce configuration values for a downstream training script.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a LoRA configuration for supervised fine-tuning
• Reviewing rank, alpha, and learning rate settings
• Choosing LoRA or QLoRA based on GPU memory

Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Design vocational training around a real work task, from preparation and equipment checks to operation, verification, and handover. Build interactive practice with tools, ordered steps, consequences for mistakes, and checkpoints where learners decide whether to proceed, recheck, or stop.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing hands-on vocational training
• Practicing equipment checks and operating steps
• Adding proceed-or-stop decisions for unsafe conditions

Plan and run authorized security tests that simulate deceptive emails, calls, text messages, and in-person approaches. Measure employee responses, compare results across test methods, and report gaps in security awareness training and access controls.

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• Planning an authorized social engineering assessment
• Measuring responses to simulated phishing
• Evaluating phone and physical access checks

Use SAELens to train and analyze sparse autoencoders, which separate neural network activity into features that may represent recognizable concepts. Explore pretrained features, evaluate reconstruction quality, and investigate how individual features influence language model outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring features with a pretrained SAE
• Training and evaluating a custom SAE
• Studying overlapping concepts in model activity

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

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• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Design and implement machine learning systems for production use. Cover data preparation, training, prediction services, model comparisons, monitoring, and retraining, with attention to reliability, response time, resource costs, and both technical and business evaluation metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a real-time recommendation service
• Comparing model versions with A/B testing
• Detecting changes in data and model performance

Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing an LLM without retraining
• Comparing pruning methods
• Exploring faster inference on supported hardware

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Prepare trained machine learning models for production with deployment, monitoring, and retraining workflows. Build RAG systems that retrieve documents for answers, and integrate language model APIs with retries, fallback providers, and cost tracking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a trained model to production
• Setting up model monitoring and retraining workflows
• Building a RAG pipeline for document-based answers