Data Science Tutorials

Learn how to set up the Kimi Browser Extension using both Kimi Work and Kimi Code CLI, and run a product search that extracts structured results from a website.

Flow matching trains generative models to turn noise into data by learning a vector field that moves samples along a chosen probability path, the same idea driving continuous normalizing flows and modern diffusion models.

Deep reinforcement learning combines the trial-and-error loop of reinforcement learning with neural networks that generalize across huge state spaces.

Learn what recursive self-improvement means in AI, how AI systems can help develop better AI, how close today's models are to RSI, and why the concept matters for AI capabilities and safety.

Learn how vision-language-action (VLA) models work, how they differ from VLMs, and how to choose between OpenVLA, pi0, and SmolVLA in 2026.

Learn how to clear formatting in Excel using the Clear Formats tool, shortcuts, and other methods while keeping your data intact.

Learn the difference between exogenous and endogenous variables, why endogeneity matters for causal inference and regression, and how to identify and address endogenous variables.

Follow this GPT-Live-1 API tutorial to build a full-duplex voice learning assistant with browser WebRTC, backend delegation, web search, and confirmed actions.

Discover how Spearman's Rank Correlation captures relationships that curve, plateau, or shift pace instead of following a straight line. Learn to calculate, interpret, and apply it in Python, R, and Excel.

Discover what AI world models are, how they differ from large language models, and how they help AI predict the future. Explore the latest 2026 industry trends.

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.