Data Science Tutorials

GGUF packages model weights, tokenizer data, and metadata into a single portable file. Learn how to choose the right quantization level and get started with Ollama.

Learn how to accelerate local Gemma 4 31B inference on a single RTX 4090 using DFlash speculative decoding and Flash Attention against a baseline setup.

A complete tutorial on using Python-native tools for async CRUD operations and HTMX interactivity.

Stop your LLM from forgetting context. Learn how to build an AI agent with episodic, semantic, and procedural memory using Spring AI and Oracle Database.

Learn how to set up Cursor automations with this hands-on tutorial covering PR code review, scheduled cron tasks, tools and MCP configuration.

Kernel density estimation is a nonparametric method for estimating the shape of a data distribution without assuming a fixed model. Learn the formula, bandwidth selection, and hands-on implementation in Python and R.

Learn how to install MySQL on Windows, macOS, and Linux. Configure your server, secure it properly, and verify the installation step by step.

A practical walkthrough of the assumptions behind logistic regression, the diagnostics that catch violations in Python and R, and the alternatives to reach for when the assumptions don't hold.

A practical guide to spline regression, covering how piecewise polynomials and knots model nonlinear relationships, the main spline types, and how to fit them in Python and R.

A practical guide to GLMs - what they are, how their three components work together, and how to fit and interpret them in Python and R.

A detailed walkthrough of overfitting and underfitting in machine learning, including how to identify each failure mode, why it happens, and how to fix it through the bias-variance tradeoff.

Learn what zero-shot classification is, how it works under the hood with NLI models, how it compares to few-shot and fine-tuning, and how to apply it with Hugging Face Transformers.