Results for “scheduling”

Plan a sprint by estimating team capacity, selecting and sequencing stories, and identifying risks.

Plan and facilitate design sprints from challenge framing through prototype testing, using a structured 5-day process with variations for mini, remote, and discovery sprints.

Battle-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.