C34 Expanding this method to higher order linear differential equations
I this video I expand the method of the variation of parameters to higher-order (higher than two), linear ODE's.
From playlist Differential Equations
Free ebook http://tinyurl.com/EngMathYT I show how to solve differential equations by applying the method of variation of parameters for those wanting to review their understanding.
From playlist Differential equations
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How To Use Newton's Method from Calculus. An easy example using the formula.
From playlist Calculus
Solving trigonometric equations with multiple angles
👉 Learn how to solve trigonometric equations. There are various methods that can be used to evaluate trigonometric equations, they include by factoring out the GCF and simplifying the factored equation. Another method is to use a trigonometric identity to reduce and then simplify the given
From playlist Solve Trigonometric Equations with Multi Angles
Solving trigonometric equations with multiple angles
👉 Learn how to solve trigonometric equations. There are various methods that can be used to evaluate trigonometric equations, they include by factoring out the GCF and simplifying the factored equation. Another method is to use a trigonometric identity to reduce and then simplify the given
From playlist Solve Trigonometric Equations with Multi Angles
A16 The method of variation of parameters
Starting the derivation for the equation that is used to find the particular solution of a set of differential equations by means of the variation of parameters.
From playlist A Second Course in Differential Equations
Solving for cosine using multiple angles
👉 Learn how to solve trigonometric equations. There are various methods that can be used to evaluate trigonometric equations, they include by factoring out the GCF and simplifying the factored equation. Another method is to use a trigonometric identity to reduce and then simplify the given
From playlist Solve Trigonometric Equations with Multi Angles
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
GTAC 2010: Flexible Design? Testable Design? You Don't Have To Choose!
Google Test Automation Conference 2010 October 28-29, 2010 Flexible Design? Testable Design? You Don't Have To Choose! Presented by Russ Rufer & Tracy Bialik, Google Inc. Abstract: Writing software is a steady stream of decisions... Should I put the next piece of functionality
From playlist GTAC 2010
Get the Awesome Code: http://goo.gl/qplBS In this part of my code refactoring tutorial, I'll show you how to replace conditionals with the command pattern. I'm also going to do more though. I'll also review what the command pattern is. I'll give an example of when you should use it. We'l
From playlist Code Refactoring
Managing The Micro - Part 3 - Towards The Last Frontier - 31st May 1981
Brian Redhead investigates flexible manufacturing and the role of the micro computer in CAD / CAM manufacture. The programme features paper punch tape in operation, IBM Computer terminals, electronic circuit board production (PCB) and a discussion between to the British Robotics Associati
From playlist Managing the Micro - BBC 2
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
18 Geostatistics Course: Machine Learning
Lecture with introduction and basic concepts related to machine learning.
From playlist Data Analytics and Geostatistics
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
Advanced Theory | Neural Style Transfer #4
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The 4th video in the neural style transfer series! 🎨 (I promise this will be the longest one 😅) You'll learn: ✔️ Ideas behind how the NST field came to be ✔️ All the amazing follow-up wo
From playlist Neural Style Transfer
New Graphene Method to Protect and Enhance Biomedical Devices
At 200 times stronger than steel, graphene has been hailed as a super material of the future ever since its discovery in 2004. The ultrathin carbon material is an incredibly strong electrical and thermal conductor, making it a perfect ingredient to enhance semiconductor chips found in many
From playlist Our Research
RubyConf 2016 - Metaprogramming? Not good enough! by Justin Weiss
RubyConf 2016 - Metaprogramming? Not good enough! by Justin Weiss If you know how to metaprogram in Ruby, you can create methods and objects on the fly, build Domain Specific Languages, or just save yourself a lot of typing. But can you change how methods are dispatched? Can you decide th
From playlist RubyConf 2016
This is How You Use the Chain Rule in Calculus
This is How You Use the Chain Rule in Calculus
From playlist Random calculus problems:)
Data-driven nonlinear aeroelastic models of morphing wings for control
In this video, Urban Fasel describes a data-driven reduced-order aeroelastic modeling technique for morphing wings and shows its applicability for model predictive control. https://royalsocietypublishing.org/doi/full/10.1098/rspa.2020.0079 Data-driven nonlinear aero
From playlist Data-Driven Science and Engineering