Mathematics is fundamental to machine learning, from probability distributions to calculus. This curated list of online tools and calculators can help you solve equations, visualize functions, and perform statistical computations quickly. Whether you’re working on probability, linear algebra, or optimization, these resources are invaluable for learning and prototyping.
Probability and Statistics Calculators
- Normal Distribution Calculator: Compute probabilities and quantiles for the normal distribution.
- t-Distribution Calculator: Calculate probabilities and critical values for t-tests.
- Chi-Square Calculator: Perform chi-square tests and find probabilities.
- Upper Tail Chi-Square Probability: Advanced chi-square distribution tool for upper tail probabilities.
- Gamma Distribution Calculator: Compute values for the gamma distribution.
Statistical Tables
- Chi-Square Probabilities Table: Reference table for chi-square critical values.
- t-Distribution Quantiles: PDF with t-distribution quantiles.
- t-Table: Printable t-distribution table.
Equation Solvers and Calculators
- Quadratic Formula Calculator: Solve quadratic equations step-by-step.
- Equation Solver: Solve simple algebraic equations.
- Online Integral Calculator: Compute definite and indefinite integrals.
- Online Derivative Calculator: Calculate derivatives of functions.
Graphing and Visualization Tools
- Symbolab Graphing Calculator: Graph 2D functions; example: input
x^2+y^2-xy=1for implicit plots. - Grapher (MacOS Built-in): Apple’s native graphing tool for 2D and 3D functions.
- Google Search Box: Quick graphing by searching “graph of [equation]” for simple visualizations.
- Desmos Graphing Calculator: Interactive online graphing for functions, inequalities, and more (recommended alternative).
LaTeX and Documentation
- LaTeX Mathematics Documentation: Comprehensive guide to writing math in LaTeX.
Additional Tips and Resources
- Wolfram Alpha: A powerful general-purpose calculator for math, statistics, and more (e.g., wolframalpha.com).
- Python Libraries: For programmatic calculations, use NumPy, SciPy, or SymPy in Python.
- Best Practices: Always verify results manually for critical computations. These tools are great for learning but may have limitations for complex problems.
- Mobile Apps: Consider apps like GeoGebra or Wolfram Alpha mobile for on-the-go calculations.
These tools can accelerate your workflow in machine learning, from understanding distributions to visualizing loss functions. If you know of other useful resources, feel free to contribute!