'machine learning' 검색 결과

Train, deploy, and run predictions with neural networks using Flow Nexus and E2B sandboxes, isolated computing environments. Use existing model templates, distribute training across multiple environments, and check training progress and model performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model to classify images
• Analyze text sentiment with an existing template
• Train an LSTM model for time series forecasting

Study and apply statistics, probability, and linear algebra through Python examples. Calculate summary statistics, compare groups, analyze A/B tests, and build regression models while checking assumptions and considering the size of observed differences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing data with averages and measures of spread
• Comparing conversion rates in an A/B test
• Comparing regression models

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Develop healthcare prediction models from electronic health records and physiological signals. Use PyHealth to prepare data, train models for tasks such as mortality or readmission prediction, and evaluate accuracy, fairness, and how predictions can be explained.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building mortality prediction models with MIMIC-IV
• Training and evaluating hospital readmission models
• Developing medication recommendation models with SafeDrug

Review candidate learnings in Claude Code's native auto memory (`~/.claude/projects/<project>/memory/`, machine-local) and run them through a five-critic council in parallel: generality, staleness, redundancy, evidence, format. Majority vote (3+ of 5) promotes the entry to MEMORY.md. Use when user says "promote memory", "review my learnings", "what should graduate to MEMORY.md", "five-critic council", or as monthly memory maintenance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use DeepXDE for scientific machine learning, PINNs, DeepONet/operator learning, backend selection, training, and troubleshooting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

View in article Table 3 Comparative analysis of selected machine learning models for stock predictio

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

예약이 완료된 Itô GPU 환경에서 기계 학습 훈련을 사용할 수 있는지 확인합니다. 현재는 훈련을 실행하지 않으며, 미제공 기능과 향후 인계 조건을 정리합니다.

• GPU 예약 후 훈련 가능 여부 확인
• 미세 조정 작업의 인계 요건 확인
• 예약, 예산, 확인 절차 정리

기계 학습·AI 연구 논문을 문헌 조사부터 실험 설계, 결과 분석, 집필, 투고 준비까지 지원합니다. 인용 검증과 심사 의견에 따른 수정도 다룹니다.

• 기존 코드와 결과로 논문 작성 시작
• 연구 주장을 뒷받침할 실험 계획
• 관련 연구와 인용 확인

Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing accuracy across training runs
• Monitoring training progress in dashboards
• Searching for better training settings

Lambda Labs에서 GPU 서버를 시작하고 머신러닝 학습과 추론을 실행하는 절차를 안내합니다. SSH 연결, 영구 저장소, Python API 조작과 여러 GPU를 활용한 분산 학습도 다룹니다.

• 장시간 학습에 사용할 전용 GPU 환경 준비
• 여러 GPU로 대규모 언어 모델 추가 학습
• 데이터셋과 학습 체크포인트 유지

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