thada2402

cv-classification

Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

cv-detection

Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

nlp-alignment

Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.

nlp-pretraining

Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.

experimental-design

Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

systematic-review

Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.

pytorch-training

Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

meta-analysis

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

Data & Documents Listed

data-loading

Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.

distributed-training

Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.

mixed-precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.