Data Science Tutorials

Gradient clipping is a one-line training fix that prevents exploding gradients from ruining deep neural network training. This guide covers how it works, the two main clipping methods, threshold selection, and implementation in PyTorch and TensorFlow.

Support Vector Regression is a margin-based regression method that ignores small errors intentionally, handles nonlinear relationships through kernels, and holds up on noisy real-world data where standard regression comes up short.

Singular Value Decomposition (SVD) is a matrix factorization method that breaks any matrix into three simpler components, revealing its underlying structure.

A conceptual guide to the kernel trick - what it is, how it enables SVMs and other kernel-based models, and when to use it over other approaches to nonlinear modeling.

A practical guide to the Kruskal-Wallis test - what it is, how it works, when to use it over ANOVA, and how to run and interpret it in Python and R.

The Mann-Whitney U test is a rank-based nonparametric test for comparing two independent groups when data doesn't meet the normality assumption required by the t-test.

GELU is a smooth, probabilistic activation function that outperforms simpler alternatives like ReLU in deep learning architectures, and has become the default choice in transformer models like BERT and GPT.

Newton's method is an iterative root-finding algorithm that uses tangent line approximations to close in on the solution of equations that have no closed-form answer.

The t statistic helps you decide whether a difference in your data is meaningful or just random variation. This guide explains how it works, how to calculate it, and how to use it in real testing scenarios with clear, step-by-step examples.

Master OpenAI's Codex CLI to automate data workflows. Learn to conduct EDA, build Python ETL pipelines, and generate tests directly from your local terminal.

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.

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.