Real numbers
Real numbers are just numbers that can be found on the number line. They include:
- Natural numbers (1, 2, 3, …)
- Whole numbers (0, 1, 2, 3, …)
- Integers (…, -2, -1, 0, 1, 2, …)
- Rational numbers (fractions like 1/2, -3/4, etc.)
- Irrational numbers (like π = 3.14159… and √2)
Basically, real numbers include all numbers you can think of, except imaginary numbers (like √-1, which isn’t on the number line).
Necessary Topics in Real Numbers for Machine Learning:
- Integers and Fractions – Used in indexing, counting, and probability.
- Decimals and Floating Points – Needed for calculations and working with data.
- Absolute Values – Important for measuring errors and distances.
- Exponents and Logarithms – Used in feature scaling and neural networks.
- Functions and Graphs – Understanding how numbers behave visually helps in data visualization.
- Limits and Continuity – Useful in optimization techniques.
- Inequalities – Used in setting up constraints in models.
Since you’re learning ML, these concepts will naturally appear as you study. If you’re comfortable with basic algebra and functions, you’re on the right track! 🚀
