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46 docs tagged with "math"

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Since I study computer science, it's okay to just take a look at this math folder... It's not that rigorous.

Central Limit Theorem

The Central Limit Theorem is a fundamental statistical principle that describes the behavior of the average of a large number of identically distributed, independent random variables.

Classification with a Neural Network

Neural networks are advanced computational models that mimic the human brain's structure, enabling them to capture and model complex, non-linear relationships between inputs and outputs. They consist of layers of perceptrons (neurons) that process inputs through weighted connections.

Definitions

Derivatives are a fundamental concept in calculus, representing the rate at which a function is changing at any given point. The formal definition and various notations are as follows:

Log Loss in Machine Learning

Machine learning often involves optimization problems that aim to minimize or maximize a particular function, known as a loss function. Two of the most common loss functions are square loss and log loss. In this note, we'll delve into log loss by exploring a probability-based example and provide the mathematical foundations for understanding it better.

Optimization using Gradient Descent - Least squares

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. The simplest form of the linear equation with one dependent and one independent variable is represented as $y = mx + b$, where $m$ is the slope of the line and $b$ is the y-intercept. This method is widely used in predictive modeling and quantitative forecasting.

Optimization using Gradient Descent in one variable

Gradient Descent is an iterative method extensively utilized in finding the minimum or maximum of functions, particularly beneficial in multi-variable scenarios. This approach is foundational in optimization problems where exact solutions are challenging to derive analytically, especially due to complexities in higher dimensions.

Optimization using Gradient Descent in two variables

Gradient descent extends to functions of multiple variables by utilizing gradients instead of derivatives. The process involves iteratively moving in the direction opposite to the gradient of the function at the current point, to find a local minimum.

Ordinary Least Squares

Linear models aim to predict a target value as a linear combination of input features. Commonly represented with:

Regression with a perceptron

Neural networks are computational models that mimic the human brain's structure to process information. They consist of units called neurons or perceptrons, which are the fundamental building blocks of neural networks. The training of these networks involves adjusting weights and biases to minimize the error in predictions, a process achieved through algorithms like gradient descent and Newton's method.

Sample Size Formula

Sample size calculation is a crucial step in designing an experiment or study, ensuring that the results are statistically significant and generalizable. Below are the main components and formulas used to calculate the sample size for different scenarios, particularly for estimating a population mean or proportion.

Statistical Power

A Gentle Introduction to Statistical Power and Power Analysis in Python - MachineLearningMastery.com