Backpropagation (Part 3a): The Chain Rule
We use the chain rule to compute the dependence of the SSE (summed squared error) on any given connection weight in a Multilayer Perceptron (MLP) using the backpropagation method. The chain rule allows us to take derivatives of a function of a function, which is essential in computing the backpropagation dependence.
Видео Backpropagation (Part 3a): The Chain Rule канала Alianna J. Maren
Видео Backpropagation (Part 3a): The Chain Rule канала Alianna J. Maren
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Backpropagation (Part 2): Mathematical Dependency and Creating the Word Problem
Three Keys to Crafting a Compelling Research Paper: Series Intro
Selecting References for Your Research Paper: Chicago Style Author-Date Formatting
Structuring Your Research Paper Part 1
Writing Your Research Paper Problem Statement
Crafting a Compelling Research Paper - Pet Peeve: Significant Figures
The New Good Stuff is at Themesis!
Career-Boosting Reading List: Part 1 - Sun Tzu's "The Art of War"
Crafting Your Research Paper Title Page
Books 2 and 3: Great Get-Life-Together Reading!
NLP: Clustering vs. Classification
Summed-Squared-Error (SSE): Neural Networks Back-Propagation X-OR Problem
Backpropagation (Part 4): The Sigmoid Transfer Function and Its Derivative
Christmas in Kaua'i - Mai Tais and AI: Part 1
Writing an Effective Research Paper: Using Storytelling and Pictures
Writing Your Research Paper Abstract to Get Reader's Attention - Part 1
Backpropagation (Part 3b): The Chain Rule (with Specific Application to the Transfer Function)
Math Bloopers! Dr. AJ's First Candid Blooper Reveal
Crafting a Compelling Research Paper - Pet Peeve: Figure Legibilitiy
AI and Society: COVID-19 Accelerates AI and Robotics Use