Data Science Tutorials

Causal machine learning combines ML with causal inference to move beyond prediction and estimate what happens when you intervene. This article covers the core concepts, methods, Python tools, and common mistakes.

Learn how state space models represent dynamic systems using hidden states and observations, including the state and observation equations, Kalman filtering, and time-series applications.

A practical guide to reviewing Python data science pull requests with Claude Code, GitHub, and ultrareview.

Learn how to build a simple LangChain4j agent that can remember important notes across conversations.

Learn how LMCache stores and reuses KV states across LLM requests, integrates with vLLM, and reduces repeated prefill computation for long-context inference.

Learn what a residual plot is, how to interpret common patterns, and how residual plots help identify nonlinearity, heteroscedasticity, outliers, and other regression problems.

A practical, math-first look at saddle points - what makes them different from local minima and maxima, how to spot one using gradients and the Hessian, and why they show up so often in high-dimensional optimization and neural network training.

Learn what an augmented matrix is, how it represents systems of equations, and how to use row operations to solve them.

Reduce token usage by cutting context bloat, terminal noise, verbose responses, and over-engineered code with lightweight tools that optimize coding-agent workflows automatically.

Learn how to organize tasks by category using Node.js and MongoDB. Build a REST API with create, list, get, update, delete endpoints and category filter.

Self-supervised learning trains models to pull their own supervision from unlabeled text, images, audio, and video, using techniques like contrastive learning, masked modeling, and autoregressive prediction to build the representations behind today's language, vision, and multimodal models.

Set up Muse Spark 1.3 in Muse Code and the Meta Model API, choose between the contributor and standard tiers, and see whether Meta's efficiency claims hold.