Nonlinear Regression and Gradient Descent

WEBSITE: databookuw.com This lecture highlights a general framework for nonlinear regression and introduces the workhorse optimization algorithm known as gradient descent.

Join Today
The Stochastic Gradient Descent Algorithm
▶︎

The Stochastic Gradient Descent Algorithm

Optimization as the cornerstone of regression
▶︎

Optimization as the cornerstone of regression

Machine Learning Lecture 12 "Gradient Descent / Newton's Method" -Cornell CS4780 SP17
▶︎

Machine Learning Lecture 12 "Gradient Descent / Newton's Method" -Cornell CS4780 SP17

Classic Curve Fitting
▶︎

Classic Curve Fitting

Regression and Ax = b: Over- and under-determined systems
▶︎

Regression and Ax = b: Over- and under-determined systems

Gradient Descent, Step-by-Step
▶︎

Gradient Descent, Step-by-Step

Gradient Descent vs Evolution | How Neural Networks Learn
▶︎

Gradient Descent vs Evolution | How Neural Networks Learn

Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
▶︎

Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)

Gaussian Processes
▶︎

Gaussian Processes

Unsupervised Learning: Hierarchical Clustering and Dendrograms
▶︎

Unsupervised Learning: Hierarchical Clustering and Dendrograms

The Misconception that Almost Stopped AI [How Models Learn Part 1]
▶︎

The Misconception that Almost Stopped AI [How Models Learn Part 1]

Intro to Gradient Descent || Optimizing High-Dimensional Equations
▶︎

Intro to Gradient Descent || Optimizing High-Dimensional Equations

Gradient Descent Explained
▶︎

Gradient Descent Explained

Curve Fitting and Regression with L1 and L2 norms
▶︎

Curve Fitting and Regression with L1 and L2 norms

16. Learning: Support Vector Machines
▶︎

16. Learning: Support Vector Machines

Building the Gradient Descent Algorithm in 15 Minutes | Coding Challenge
▶︎

Building the Gradient Descent Algorithm in 15 Minutes | Coding Challenge

Distributed Optimization via Alternating Direction Method of Multipliers
▶︎

Distributed Optimization via Alternating Direction Method of Multipliers

Optimal Basis Elements: The POD Expansion
▶︎

Optimal Basis Elements: The POD Expansion

Model selection: Cross validation
▶︎

Model selection: Cross validation

Steepest Descent Method (Unconstrained Optimization)
▶︎

Steepest Descent Method (Unconstrained Optimization)