Math Lab

How to use

What it does

A single page bundling 16 related tools — Functions and constants, Complex numbers, matrices, vectors, big integers and primes, Mortgage / car loan monthly payment, Compute P(n,k) and C(n,k), Standard normal distribution query, One-sample t-test and p-value, Fit a line to scatter points, Estimate π from needles crossing parallel lines., Sample from a normal distribution and view the histogram., Estimate π by throwing darts into a circle inside a square., Generate a sequence from a custom transition matrix., Molar mass and mass-to-moles for simple formulas. and Balance simple A + B -> C equations by atom conservation.. Switch with the tabs above; each tool is fully usable here. Merged tabs add bitwise operations, IEEE 754 bit layout and radix conversion tools on demand.

How to use it

Pick a tab to load the matching tool. Everything you type stays in your browser; nothing is uploaded or saved on a server.

Deep Learning Notes-1: Polynomial Fitting, Probability Theory, and Information Theory Fundamentals

Deep Learning Notes-1, covering overfitting and underfitting, regularization, probability theory fundamentals (Bayes' theorem, Gaussian distribution, maximum likelihood estimation), and information theory (entropy and KL divergence). Corresponds to Chapters 1-2 of "Deep Learning: Foundations and Concepts".

Deep Learning Notes-2: Probability Distributions, Linear Regression, and the Bias-Variance Tradeoff

Deep Learning Notes-2, covering the Bernoulli distribution, multinomial distribution, multivariate Gaussian distribution, the probabilistic derivation of linear regression, decision theory, and the bias-variance tradeoff. Corresponds to Chapters 3-4 of "Deep Learning: Foundations and Concepts".

Deep Learning Notes - 7: Sampling Methods and Latent Variable Models

Deep Learning Notes - 7, covering sampling methods (Monte Carlo, MCMC, Gibbs sampling), discrete latent variables (K-means, GMM, EM algorithm), and continuous latent variables (PCA, probabilistic PCA). Corresponding to Chapters 14-16 of "Deep Learning: Fundamentals and Concepts".

Numbers, Randomness, and Probability

IEEE 754, Monte Carlo, entropy, and prime stripes

Generative Systems

Cellular automata, Markov chains, and procedural generation

Life and Science Tools

Formulas, rhythms, balancing, and significance tests

Generators

UUID, NanoID/Snowflake IDs, secure passwords and MD5/SHA-256 hashes in one page.

Algorithm Visualizer

Visual demos of sorting, searching trees, dynamic programming and graph algorithms — BST, DP, graphs and shortest paths in one place.

Health Toolkit

BMI, calories, fitness calculators, ovulation tracking and BAC estimation — everyday health numbers in one toolkit.