machine learning

in supply-chain planning has traditionally been treated as a time-series problem. Each SKU is modeled independently. A rolling time window (say, last 14 days) is used to predict tomorrow’s sales. Seasonality is captured, promotions are added,...

What motivates you to take dense academic concepts (like Stochastic Differential Equations) and switch them into accessible tutorials for the broader TDS community? It’s natural to wish to learn all the pieces in its natural...

of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result's global order emerging from local consistency. Now imagine...

Explainability in AI is important for gaining trust in model predictions and is extremely essential for improving model robustness. Good explainability often acts as a debugging tool, revealing flaws within the model training process....

, I worked on real-time fraud detection systems and suggestion models for product corporations that looked excellent during development. Offline metrics were strong. AUC curves were stable across validation windows. Feature importance plots told...

is a to Optimizing Data Transfer in AI/ML Workloads where we demonstrated using NVIDIA Nsight™ Systems (nsys) in studying and solving the common data-loading bottleneck — occurrences where the GPU idles while it waits for input...

I actually have been working within the Analytics space for over 20 years. Back then, it was not called “analytics”, it was “Business Intelligence” and even “Decision Support Systems” in older times. The terms...

I the concept of federated learning (FL) through a comic by Google in 2019. It was a superb piece and did a fantastic job at explaining how products can improve without sending user...