'machine learning' 검색 결과

Monitor machine learning training through WandB or training logs. Check for invalid numbers, worsening metrics, and stalled improvement, then report whether to continue, wait, or stop, preserving evidence and notes when stopping a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring loss and evaluation metrics during training
• Investigating invalid numbers or diverging training
• Assessing whether stalled training should continue

Develop mathematical proofs for machine learning and AI theory by checking assumptions, intermediate results, and edge cases. Produce a complete proof, a corrected claim with its proof, or a report explaining the unresolved gap.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a proof of a theorem or lemma
• Expand a proof sketch into justified steps
• Check for hidden assumptions and counterexamples

Launch machine learning experiments on local GPUs, remote servers, Vast.ai, or Modal. It checks the configured environment, deploys code, saves logs, and verifies startup, with optional W&B metric logging and automatic Vast.ai result collection and instance cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run training jobs on a local GPU
• Deploy experiments to an SSH server
• Run experiments on Vast.ai and collect results

Run batches of machine learning experiments on remote GPUs over SSH, varying settings and random seeds. The skill schedules jobs, tracks progress, retries GPU memory failures, and cleans up leftover sessions before starting subsequent batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run experiments across multiple random seeds
• Test combinations of model settings
• Chain teacher training and student distillation

Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing the contribution of individual method components
• Planning additional experiments before paper submission
• Prioritizing experiments within a GPU budget

Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare model performance across experimental conditions
• Check variation across runs with different random seeds
• Identify trends as a parameter changes

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a method section into an architecture diagram
• Create comparison charts from experiment results
• Plot training loss and accuracy over time

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add text classification or translation to a browser app
• Implement image classification and object detection
• Transcribe audio using JavaScript

Draft and revise machine learning, computer vision, and natural language processing papers. Improve section structure, paragraph flow, and figures or tables, then check major claims against experimental evidence and review the draft from a skeptical reviewer’s perspective.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting or revising an abstract and introduction
• Improving paragraph flow and terminology consistency
• Checking whether experiments support major claims

Find GitHub projects using keywords, minimum stars, and update dates, then collect repository details and contributor profiles through BrowserAct. Results include project descriptions, contributor biographies, personal websites, and Twitter handles for research, recruiting, or collaboration scouting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Discover open-source projects in a specific field
• Collect developer profiles for recruiting
• Find potential collaborators and sponsors

Add datasets, models, and other components to machine learning projects using consistent registration and creation patterns. The skill guides file placement, configuration, and automatic module loading so new components fit the project's established architecture.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a registered Dataset class
• Creating a model driven by configuration
• Wiring registration and creation logic in a new module

Guide bug bounty work from asset discovery to validation and reporting, while resolving local locations of security tools, wordlists, and cloned projects. It covers web and AI security testing with an emphasis on demonstrated impact.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate installed security tools and wordlists
• Plan reconnaissance within a program's scope
• Investigate related weaknesses and their combined impact

Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Debugging MATLAB signal-processing scripts
• Analyzing sensor time series in Python
• Checking units and assumptions in optical simulations

Turn machine learning research code and experimental results into conference paper drafts. Organize claims and evidence, find related studies, verify references through academic services, and adapt manuscripts using LaTeX templates and submission checklists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a paper from research code and results
• Finding related studies and comparison methods
• Verifying references and retrieving BibTeX

Add datasets, models, and other components to machine learning projects using consistent registration and creation patterns. The skill guides file placement, configuration, and automatic module loading so new components fit the project's established architecture.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a registered Dataset class
• Creating a model driven by configuration
• Wiring registration and creation logic in a new module

Create figures for machine learning papers from text or experiment data. Uses Gemini for architecture diagrams and matplotlib/seaborn for numerical charts, choosing suitable layouts and chart types while preparing consistent styling, export files, and reusable generation scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a methods section into an architecture diagram
• Charting model comparisons and ablation results
• Plotting training curves and confusion matrices

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Build PyTorch machine learning workflows for molecules, proteins, and biomedical relationships. Get guidance on datasets, model selection, training, and evaluation for property prediction, protein modeling, molecule generation, and planning synthesis routes from target molecules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting molecular solubility and toxicity
• Predicting protein function from sequences or structures
• Exploring new drug uses through biomedical relationships

Helps plan dataset splits for machine learning training, validation, and testing. Specifies ratios, random seeds, class balance rules, and time or group constraints, with guidance for handing the split to leakage review and model training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning training, validation, and test partitions
• Preserving class proportions across dataset splits
• Defining time-based or grouped split constraints