Skip to navigation
Skip to main content
Skip to primary sidebar
Skip to secondary sidebar
Skip to footer

Category Archives: Theory

Derivation: Error Backpropagation & Gradient Descent for Neural Networks

Posted by dustinstansbury

The material in this post has been migraged with python implementations to my github pages website.

Posted in Algorithms, Classification, Derivations, Gradient Descent, Machine Learning, Neural Networks, Optimization, Regression, Theory

Tags: backprop derivation, backpropagation algorithm, backpropagation derivation, Derivation, Machine Learning, Neural Networks

Model Selection: Underfitting, Overfitting, and the Bias-Variance Tradeoff

Posted by dustinstansbury

The material in this post has been migrated with python implementations to my github pages website.

Posted in Regression, Simulations, Statistics, Theory

Tags: bias-variance decomposition, bias-variance tradeoff, dependent variable, estimator, estimator bias, estimator variance, independent variable, learning curve, polyfit.m, polynomial model, Regression, Simulation, testing error, testing set, training error, training set

Derivation: Ordinary Least Squares Solution and Normal Equations

Posted by dustinstansbury

The material in this post has been migrated to a post by the same name on my github pages website.

Posted in Derivations, Regression, Statistics, Theory, Uncategorized

Tags: Derivation of OLS Solution, Mean Squared Error, Normal Equations, OLS

Cutting Your Losses: Loss Functions & the Sum of Squares Loss

Posted by dustinstansbury

The material for this post has been migrated to my GitHub pages site. See you there!

Posted in Regression, Statistics, Theory

Tags: Least Squares, Linear Models, Loss Functions, Parameter Estimation, Regression

Design a site like this with WordPress.com