Time-Series Forecasting: Beyond ARIMA
Transformers and probabilistic models for better forecasts.
Vision Transformers vs CNNs
Where each architecture shines in real-world vision apps.
Deploying Deep Learning on the Edge
Quantization and pruning tips to fit big models into tiny boxes.
RAG Systems: Retrieval that Actually Works
Practical chunking, indexing, and eval so your RAG doesn't hallucinate.
From Zero to Hero: Building Your First ML Pipeline
Walkthrough from raw data to deployment-ready model, step by step.
Prompt Engineering Playbook for Reliable LLMs
Patterns, guardrails, and evaluation techniques that keep outputs on target.
Training at Scale: Distributed PyTorch
DDP gotchas, checkpointing, and profiling that saves weekends.
SQL for Data Scientists
Window functions, CTEs, and anti-joins you’ll reuses.
Small Data, Big Insights
Robust stats and careful validation when data is scarce.
Feature Stores 101
Consistent features that speed up ML velocity and prevent leakage.