deep-learning
De la autor
Use when training or debugging a neural net in PyTorch — the forward/loss/backward/step loop and its silent bugs, mixed precision (AMP), AdamW/LR schedules, DDP/FSDP/ZeRO, checkpoints and seeds. NOT LoRA/QLoRA on a pretrained LLM (that is `finetuning`), NOT tabular sklearn/XGBoost (that is `machine-learning`), NOT tokenization or NLP metrics (that is `nlp`).
Instalare
npx skills add ericrisco/rsc-harness --skill deep-learning -a claude-code
Se instalează în .claude/skills/deep-learning/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 85
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- pytorch-lightningOrchestra-Research/AI-Research-SKILLsHigh-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.Inginerie★ 13.9K
- machine-learningericrisco/rsc-harnessUse when predicting a column from rows of tabular features with classic models — scikit-learn pipelines, RandomForest, XGBoost/LightGBM, leak-free cross-validation, metrics for imbalanced classes, or a model that aced CV then collapsed in production. NOT PyTorch neural nets (that is `deep-learning`), NOT cleaning the dirty table first (that is `data-cleaning`), NOT forecasting a dated series (that is `forecasting`), NOT text/token modeling (that is `nlp`).Altele★ 255
- deep-learning-pytorchMindrally/skillsExpert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.Altele★ 286
- ml-training-recipesOrchestra-Research/AI-Research-SKILLsBattle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.Știință★ 13.9K
- deep-learning-pythonMindrally/skillsGuidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.Inginerie★ 286
- fine-tuning-expertJeffallan/claude-skillsUse when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.DevOps★ 12K
Note și recenzii
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