#ML
Collection of 7 technical entries associated with this specific classification.
Statistics for ML: Evaluation and Inference
Move beyond simple accuracy. Explore the statistical foundations of model evaluation: from hypothesis testing and confidence intervals to the fundamental bias‑variance tradeoff.
Optimization for ML: Beyond Gradient Descent
This article explores the optimization landscape: from convex to non‑convex problems, from first‑order to second‑order methods, and from simple SGD to adaptive algorithms like Adam and RMSprop.
Probability for ML From Bayes' Theorem to Generative Models
Probability theory is the mathematics of uncertainty—and machine learning is all about making decisions under uncertainty. This article demystifies the probabilistic foundations that underpin everything from spam filters to large language models.
Calculus for ML: Gradients, Optimization and the Chain rule
If linear algebra provides the vocabulary of machine learning, calculus provides the grammar—it tells us how things change. This article demystifies the calculus concepts that power modern ML
Linear Algebra in ML From Matrices to Embeddings
Linear algebra forms the fundamental language of modern machine learning. This article explores how seemingly abstract concepts—vectors, matrices, decompositions—materialize into practical applications ranging from dimensionality reduction to semantic representation learning.
Beyond Tradition: Harnessing Machine Learning for Demand Forecasting
What happens when traditional time series models aren't enough? In Part 3 of our MLOps series, we unlock the power of Machine Learning for demand forecasting by incorporating promotions, holidays, and product categories. Sometimes, context is everything..
Beyond PSM: A Tour of the Causal Inference Toolkit (DiD, IV, RDD)
PSM is great, but it can't solve every problem. What about unobserved confounders? Or when you have a clear before-and-after?