Gaussian Distribution
In machine learning, the Gaussian distribution, is also known as the normal distribution. It is a continuous probability distribution function that is symmetrical at the mean, and the majority of data falls within one standard deviation of the mean. It is characterized by its bell-shaped curve.
Gaussian Distribution Formula
The PDF (probability density function) of the Gaussian distribution is given by the formula:
[Tex]f(x) = \frac{1}{\sigma \sqrt{2\pi}} \exp \left( -\frac{(x – \mu)^2}{2\sigma^2} \right) [/Tex]
where:
- x represents the Variable
- μ represents the Mean
- σ represents the Standard Deviation
- e represents the base of the Natural Logarithm.
Gaussian Distribution In Machine Learning
The Gaussian distribution, also known as the normal distribution, plays a fundamental role in machine learning. It is a key concept used to model the distribution of real-valued random variables and is essential for understanding various statistical methods and algorithms.
Table of Content
- Gaussian Distribution
- Gaussian Distribution Curve
- Gaussian Distribution Table
- Properties of Gaussian Distribution
- Machine Learning Methods that uses Gaussian Distribution
- Implementation of Gaussian Distribution in Machine Learning
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