'learning' 검색 결과

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manage project learnings. Review, search, prune, and export what gstack has learned across sessions. Use when asked to "what have we learned", "show learnings", "prune stale learnings", or "export learnings". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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

복잡한 작업과 실패에서 근본 원인, 다음에 취할 행동, 문제를 알아볼 징후를 추출해 재사용할 학습 기록으로 정리합니다. 원인이 같은 기존 기록과의 중복도 정리합니다.

• 복잡한 작업 후 배운 점 기록
• 장애 조사의 원인과 교훈 정리
• 근본 원인이 같은 학습 기록 통합

대화와 성과에 따라 행동을 조정하는 에이전트를 만들고 학습 현황, 효율, 정확도를 확인합니다. 피드백에 따른 조정과 에이전트 간 지식 공유도 수행합니다.

• 학습 설정을 갖춘 에이전트 생성
• 학습 진행 상황과 효율·정확도 확인
• 피드백에 따른 행동 조정

성공한 작업의 절차와 교훈을 학습 패턴으로 저장하고 장기 보관을 위해 통합합니다. 분야별 추가 학습, 저장소별 학습 결과 확인, 저장 데이터 압축도 지원합니다.

• 성공한 작업의 교훈 저장
• 누적된 작업 이력 통합
• 분야별 추가 학습 데이터 생성

Claude Code의 최근 대화에서 개발 강점과 반복되는 어려움을 분석합니다. 우선순위가 있는 개선안과 관련 학습 글을 보고서로 정리해 본인의 Slack 개인 메시지로 보냅니다.

• 최근 대화로 개발 습관을 돌아봅니다
• 반복되는 기술 과제의 우선순위를 정합니다
• 현재 업무에 맞는 학습 글을 찾습니다

Learn AI engineering by building a project one stage at a time. The tutor guides predictions, learner-written code, automated checks, and reflection, then records progress so the next session can continue from the right stage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn AI engineering through hands-on projects
• Resume a project from the next unfinished stage
• Find coding mistakes with step-by-step hints

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

Design subject lessons that also teach students how to learn. Add recall practice, explanations in their own words, predictions, and reflection to help learners check their understanding and plan later review while studying the main concepts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding recall practice to subject lessons
• Having students explain concepts in their own words
• Designing feedback and retries after mistakes

Prepare for an upcoming workday by combining Outlook meetings with open tasks and workspace context. Generate an HTML briefing with meeting preparation, scheduling conflicts, suggested priorities, and available time for learning and focused work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare for tomorrow's meetings
• Spot overlapping meetings and scheduling issues
• Find time for learning and focused work

Build machine-learning trading signals from price and volume data. The workflow checks inputs, creates predictive features, and repeatedly trains on available historical data to produce bounded signals indicating predicted direction and strength.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building trading signals from price and volume data
• Comparing machine-learning models
• Testing expanding and sliding training windows

Build normal traffic profiles for industrial control networks and identify unusual connections, polling intervals, and protocol operations. Use machine learning and process records to investigate deviations and produce anomaly reports with severity levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Establishing normal traffic profiles for industrial networks
• Detecting unusual connections and polling intervals
• Investigating Nozomi Guardian or Dragos alerts

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure reinforcement learning for large MoE models
• Investigate instability in FP8 training
• Set up speculative decoding for faster rollout generation

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrate single-cell RNA datasets and correct batch effects
• Analyze paired RNA and protein or RNA and ATAC measurements
• Transfer cell-type labels from reference data

Choose Swedish videos and podcasts suited to your approximate language level and learning goals. Build a manageable plan for listening, reading, writing, and speaking, with specific resources and short exercises to practice each skill.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing Swedish resources suited to your level
• Planning podcast practice for a daily commute
• Practicing pronunciation by repeating video dialogue

Plan smart contract audits using research from security organizations. Combine threat modeling, static code analysis, fuzzing that varies test inputs, and manual review, while using attack patterns and a four-phase roadmap to guide further learning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a smart contract security audit
• Setting up Echidna or Medusa fuzzing
• Reviewing attack patterns and past audit findings

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses