Data Science Tutorials

A practical guide to geometric series covering the finite and infinite sum formulas, convergence conditions, and real-world applications across finance, physics, and computer science.

Learn how to run GLM 5.1 locally on an H100 GPU with llama.cpp, test it, use the WebUI, and integrate Claude Code.

Learn how to install Google's new AI-optimized CLI, authenticate it, and wire it up to a Gemini-powered RAG pipeline that answers questions about your own Drive files.

A practical guide to Maclaurin series covering the core formula, common expansions, convergence rules, and real-world applications in numerical methods, physics, and machine learning.

Learn how to use the SQL RANK() function to assign rankings to rows, handle ties, and compare it with DENSE_RANK() and ROW_NUMBER().

Learn how to install and set up Hermes Agent, the open-source AI agent by Nous Research that remembers, learns, and grows smarter with every task.

This step-by-step guide teaches you to build a secure local AI agent with NanoClaw on Windows, connect it to WhatsApp, and control everything from the chat.

Learn how to run Meta's TRIBE V2 model on Google Colab, predict cortical activity from naturalistic stimuli, and visualize the results as interactive 3D brain heatmaps.

Learn how to create and use pivot tables in Google Sheets to summarize, analyze, and explore your data.

The F statistic is used to test whether a model explains variation in the data better than random chance. This guide explains what the F statistic means, how it is calculated, and how to interpret it.

Learn how to fine-tune Gemma 4 E4B-it on a human emotions dataset using LoRA, 4-bit quantization, and a single 3090 GPU.

Learn what an objective function is, how it works in optimization and machine learning, and how to define and interpret it with real examples.