Vision Transformers vs CNNs

Window functions, CTEs, and anti-joins you’ll reuses.

Transformers and probabilistic models for better forecasts.

Noise schedules, U-Nets, and guidance—explained simply.

Consistent features that speed up ML velocity and prevent leakage.

Robust stats and careful validation when data is scarce.

DDP gotchas, checkpointing, and profiling that saves weekends.

Patterns, guardrails, and evaluation techniques that keep outputs on target.

Walkthrough from raw data to deployment-ready model, step by step.

Practical chunking, indexing, and eval so your RAG doesn't hallucinate.

Quantization and pruning tips to fit big models into tiny boxes.

Where each architecture shines in real-world vision apps.