'machine learning' 검색 결과

Build and optimize quantum circuits with PennyLane, including models that combine quantum circuits with PyTorch or JAX. Explore molecular energy estimation and optimization problems, validate values and gradients in simulation, and prepare tested circuits for provider plugins.

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• Check quantum circuit values and gradients in simulation
• Train hybrid quantum and PyTorch or JAX models
• Estimate molecular energies with VQE

Process satellite imagery and map data for geospatial research. Helps align coordinate systems and image grids, calculate vegetation and terrain measures, summarize data by area, and analyze road networks while preserving masks and source information.

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• Align coordinate systems and raster grids
• Calculate vegetation indices from satellite imagery
• Analyze slope and aspect from elevation data

Find scientific datasets, AI models, methodology posts, and interactive demos through the Hugging Science catalog. Compare candidates for a research task and verify their licenses, input formats, access requirements, and supported ways to run them.

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• Finding models for protein or genome analysis
• Selecting datasets for scientific machine learning
• Comparing resource licenses and runtime requirements

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

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• Classify time series and predict continuous outcomes
• Group time series with similar patterns
• Forecast future values

Prepare genomic interval data for machine learning by checking BED files, reference genome consistency, and model compatibility. Plan Region2Vec or scEmbed workflows and assess shared reference regions using local validation and planning helpers.

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• Check BED coordinates and reference genome consistency
• Plan Region2Vec or scEmbed training
• Inspect model and tokenizer compatibility

Check ten recurring problems before or during machine learning runs on NVIDIA DGX Spark. Diagnose startup failures, out-of-memory errors, and falling throughput, with guidance on compatible environments, resource use, numeric formats, and training across two devices.

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• Checking a DGX Spark before a long training run
• Diagnosing memory errors despite apparent free capacity
• Investigating throughput drops during training

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.

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

Analyze DNS logs for signs of hidden command traffic and data theft. Use character randomness, query timing, TXT record inspection, and machine learning to identify suspicious domains and support security monitoring rules.

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• Investigating suspected DNS tunneling
• Classifying algorithmically generated domains
• Detecting periodic DNS beacon traffic

Inspect DNP3 traffic, a communication protocol used in industrial control systems, for unauthorized sources and unexpected commands. The skill compares captured traffic with normal patterns and reports suspicious control actions, restarts, and file operations.

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• Investigating suspicious DNP3 control commands
• Checking substation traffic against normal patterns
• Preparing DNP3 intrusion detection rules

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

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• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Set up AI email screening that compares writing style and communication habits to identify impersonation and suspicious payment requests. Connect detection policies to quarantine, warning banners, and analyst review workflows.

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• Detecting executive impersonation in emails
• Reviewing vendor payment change requests
• Flagging unusual employee email behavior

Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.

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• Building continuous monitoring for insider threats
• Scoring unusual logins and large downloads
• Comparing behavior across similar job roles

Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.

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• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

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.

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

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

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• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

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• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

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.

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

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.

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

Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.

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• Design a data analysis system with multiple AI agents
• Choose Google Cloud products for analytical requirements
• Prepare an architecture diagram and deployment guide

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Find arXiv papers by topic, research category, or paper ID. Retrieve titles, authors, abstracts, and links, check new submissions, and add AlphaXiv's AI-generated overviews when available to help decide which papers to read.

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• Checking new AI and machine learning papers
• Finding papers on a specific research topic
• Looking up a paper by its ID or arXiv URL

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.

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

Launch machine learning training jobs on local or remote GPUs. The skill checks available GPU memory, synchronizes code, saves logs, and verifies startup, with optional experiment tracking and support for Vast.ai and Modal.

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• Launch training on an available remote GPU
• Run experiments locally with CUDA or MPS
• Record training metrics with W&B