A Crash Course of Model Calibration – Part 1
How to make ML models reflect true probabilities in their predictions?
Daily Dose of Data Science
Every week, we share practical no-fluff deep dives on topics that truly matter to your skills for succeeding and staying relevant in ML & DS roles.
Conformal Predictions: Build Confidence in Your ML Model's Predictions
A critical step towards building and using ML models reliably.
Quantization: Optimize ML Models to Run Them on Tiny Hardware
A must-know skill for ML engineers to reduce model footprint and inference time.
HDBSCAN: The Supercharged Version of DBSCAN — An Algorithmic Deep Dive
A beginner-friendly introduction to HDBSCAN clustering and how it is superior to DBSCAN clustering.
A Crash Course on Causality – Part 2
A guide to building robust decision-making systems in businesses with causal inference.
A Crash Course on Causality – Part 1
A guide to building robust decision-making systems in businesses with causal inference.
Implementing KANs From Scratch Using PyTorch
A step-by-step demonstration of an emerging neural network architecture — KANs.
