“machine learning”的搜索结果

Selects biomarker features from high-dimensional omics data using Boruta all-relevant selection, mRMR, LASSO/elastic-net, and stability selection, while controlling the leakage, irreproducibility, and correlated-feature traps that make most published signatures fail to replicate. Use when identifying candidate biomarkers, deciding between an all-relevant and a minimal-optimal selector, or judging whether a selected gene set is reproducible. For unbiased performance estimation of the resulting model see machine-learning/model-validation; for interpreting a trained model see machine-learning/prediction-explanation.

中文简介正在准备中。当前显示原文说明。

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

中文简介正在准备中。当前显示原文说明。

Machine Learning integration for Flutter using TensorFlow Lite and Firebase ML

中文简介正在准备中。当前显示原文说明。

Machine Learning integration for Flutter using TensorFlow Lite and Firebase ML

中文简介正在准备中。当前显示原文说明。

Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

中文简介正在准备中。当前显示原文说明。

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

中文简介正在准备中。当前显示原文说明。

Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

中文简介正在准备中。当前显示原文说明。

Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

中文简介正在准备中。当前显示原文说明。

Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

中文简介正在准备中。当前显示原文说明。

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

中文简介正在准备中。当前显示原文说明。

Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.

中文简介正在准备中。当前显示原文说明。

Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

中文简介正在准备中。当前显示原文说明。

帮助将机器学习实验整理为生产系统，明确数据规格、可复现训练、质量评估、部署、监控与回退，并形成实施计划和审查事项。

• 将实验代码整理为训练流水线
• 制定模型发布前的质量标准
• 调查数据泄漏与预处理差异

使用云端GPU运行机器学习训练、推理和数据处理，并将模型发布为API。通过Python定义运行环境，还可配置自动扩缩容、存储、并行处理和定时任务。

• 无需管理GPU服务器即可训练模型
• 将模型发布为自动扩缩容的API
• 并行处理大量数据

Learn to examine compiled programs through one interactive lesson per session. Labs and quizzes cover file formats, machine instructions, program behavior, and analysis tools, while recorded observations provide evidence for completing each lesson and resuming later.

中文简介正在准备中。当前显示英语简介。

• Learn to read programs you compiled yourself
• Study binary formats and machine instructions
• Check analysis claims against observed output

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

中文简介正在准备中。当前显示英语简介。

• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.

中文简介正在准备中。当前显示英语简介。

• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

中文简介正在准备中。当前显示英语简介。

• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.

中文简介正在准备中。当前显示英语简介。

• Plan AI features and language model integration
• Organize RAG retrieval design and implementation
• Map agent roles, tools, and memory

Search AI and machine learning jobs through AI Dev Jobs MCP, a connection agents use to access job data. Inspect employers and requirements, match roles to skills and preferences, and review available salary and market statistics.

中文简介正在准备中。当前显示英语简介。

• Finding remote machine learning jobs
• Discovering companies hiring for AI roles
• Matching jobs to skills and work preferences

Design machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.

中文简介正在准备中。当前显示英语简介。

• Designing an end-to-end machine learning pipeline
• Building reproducible training workflows
• Comparing models before production deployment

Examine recorded calls and voicemails for signs of AI-generated speech. Extract acoustic features, classify samples with machine learning, and prepare reports with confidence scores and sound visualizations to support voice impersonation investigations.

中文简介正在准备中。当前显示英语简介。

• Investigating calls impersonating executives
• Checking suspicious voicemails
• Comparing suspect recordings with genuine voice samples

Analyze authentication logs to spot suspicious sign-ins, password attacks, and possible account compromise. Statistical baselines and machine learning help compare login activity with normal user behavior, while example SIEM queries support security investigations.

中文简介正在准备中。当前显示英语简介。

• Investigating suspicious sign-in alerts
• Detecting geographically inconsistent logins
• Finding brute force and password spraying

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

中文简介正在准备中。当前显示英语简介。

• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel