Lagrange multiplier example: Minimizing a function subject to a constraint
Free ebook http://tinyurl.com/EngMathYT I discuss and solve a simple problem through the method of Lagrange multipliers. A function is required to be minimized subject to a constraint equation. Such an example is seen in 2nd-year university mathematics.
From playlist Lagrange multipliers
Computing Limits from a Graph with Infinities
In this video I do an example of computing limits from a graph with infinities.
From playlist Limits
Linear regression (6): Regularization
Lp regularization penalties; comparing L2 vs L1
From playlist cs273a
How to Determine if Functions are Linearly Independent or Dependent using the Definition
How to Determine if Functions are Linearly Independent or Dependent using the Definition If you enjoyed this video please consider liking, sharing, and subscribing. You can also help support my channel by becoming a member https://www.youtube.com/channel/UCr7lmzIk63PZnBw3bezl-Mg/join Th
From playlist Zill DE 4.1 Preliminary Theory - Linear Equations
How to solve a word problem for linear programming
Learn how to solve problems using linear programming. A linear programming problem involves finding the maximum or minimum value of an equation, called the objective functions, subject to a system of inequalities, called the constraints. To solve a linear programming problem graphically,
From playlist Solve Linear Programming Problems #System
V4-05. Linear Programming. Definition of the Dual problem. Part 4
Math 484: Linear Programming. Definition of the Dual problem. Part 4 Wen Shen, 2020, Penn State University
From playlist Math484 Linear Programming Short Videos, summer 2020
Graphing a linear system of linear inequalities
👉 Learn how to graph a system of inequalities. A system of inequalities is a set of inequalities which are collectively satisfied by a certain range of values for the variables. To graph a system of inequalities, each inequality making up the system is graphed individually with the side of
From playlist Solve a System of inequalities by Graphing | Standard Form
Summary for graph an equation in Standard form
👉 Learn about graphing linear equations. A linear equation is an equation whose highest exponent on its variable(s) is 1. i.e. linear equations has no exponents on their variables. The graph of a linear equation is a straight line. To graph a linear equation, we identify two values (x-valu
From playlist ⚡️Graph Linear Equations | Learn About
Initializers Activations Regularizers And Constraints - Keras
In this video, we go over initializers, activations, regularizers and constraints - all of which are essentially used to make layers bigger and better. I first explain the usage of initializers which can be used for any variable. Initialization is incredibly important because we are deal
From playlist A Bit of Deep Learning and Keras
Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 12 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/c
From playlist Machine Learning Course - CS 156
8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair How to solve the example linear optimization problem using the software, LibreOffice. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/t
From playlist MIT 15.071 The Analytics Edge, Spring 2017
Regularization (Machine Learning): Georg Gottwald
Machine Learning for the Working Mathematician: Week Four 17 March 2022 Georg Gottwald, Regularization Seminar series homepage (includes Zoom link): https://sites.google.com/view/mlwm-seminar-2022
From playlist Machine Learning for the Working Mathematician
8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Changing the optimization formulation to include connecting flights to solve a more complicated problem. License: Creative Commons BY-NC-SA More information at ht
From playlist MIT 15.071 The Analytics Edge, Spring 2017
Stephen Wright: "Sparse and Regularized Optimization, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "Sparse and Regularized Optimization, Pt. 2" Stephen Wright, University of Wisconsin-Madison Institute for Pure and Applied Mathematics, UCLA July 17, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning
Seventh Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, December 2, 10:00am EDT Speaker: Martin Burger, FAU Title: Nonlinear spectral decompositions in imaging and inverse problems Abstract: This talk will describe the development of a variational theory generalizing classical spectral decompositions in linear filters and si
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
V4-06. Linear Programming. Examples and interpretations of duality.
Math 484: Linear Programming. Examples and interpretations of duality. Wen Shen, 2020, Penn State University
From playlist Math484 Linear Programming Short Videos, summer 2020
Evrim Acar - Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations
Recorded 11 January 2023. Evrim Acar of Simula Research Laboratory presents "Extracting Insights from Complex Data: Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: In or
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
Phase transitions of random constraint satisfaction problems – Allan Sly – ICM2018
Probability and Statistics Invited Lecture 12.5 Phase transitions of random constraint satisfaction problems Allan Sly Abstract: Random constraint satisfaction problems encode many interesting questions in the study of random graphs such as the chromatic and independence numbers. Ideas f
From playlist Probability and Statistics
V4-02. Linear Programming. Definition of the Dual problem.
Math 484: Linear Programming. Definition of the Dual problem. Wen Shen, 2020, Penn State University
From playlist Math484 Linear Programming Short Videos, summer 2020