-
What Is Mathematical Optimization?
A gentle and visual introduction to the topic of Convex Optimization. (1/3)
This video is the first of a series of three. The plan is as follows:
Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M)
Part 2: Convexity and the Principle of (Lagrangian) Duality (https://youtu.be/d0CF3d5aEGc)
Part 3: Algorithms for Convex Optimization (Interior Point Methods). (https://youtu.be/uh1Dk68cfWs)
-------------------------------
Typos:
- 8:34, The matrix A should be of size nxm, and the vector b should be of size 1xm.
--------------------------------
Timestamps:
0:00 Intro
2:50 What is optimization?
06:00 Linear programs
8:19 Linear regression
9:32 (Markovitz) Portfolio optimization
10:00 Conclusion
--------------------------
Credit:
🐍 Manim and Python : https://gi...
published: 27 Jun 2021
-
2: What is Mathematical Optimization?
--
Learn more about Gurobi Optimization here:
https://www.gurobi.com/
Check out our Optimization Application Demos here:
https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,400 customers:
https://www.gurobi.com/customers/example-customers/
--
About Gurobi
Gurobi produces the world’s fastest and most powerful mathematical optimization solver – the Gurobi Optimizer – which is used by leading global companies across more than 40 different industries to rapidly solve their complex, real-world problems and make automated decisions that optimize their efficiency and profitability.
As the market leader in mathematical optimization software, we aim to deliver not only the best solver, but also the best support – so that companies can fully leverage the power of mathemati...
published: 09 Apr 2020
-
Optimization Problems - Calculus
This calculus video explains how to solve optimization problems. It explains how to solve the fence along the river problem, how to calculate the minimum distance between a point and a line, and how to maximize area while minimizing perimeter as in the case of fencing problems.
Get The Rest of the Video on Patreon:
https://www.patreon.com/MathScienceTutor
Direct Link To Part 2: Questions 13 thru 31:
https://bit.ly/2Y3Aem5
Derivative Applications - Free Formula Sheet:
https://bit.ly/4eV6r1b
______________________________
Join The Membership Program:
https://www.youtube.com/channel/UCEWpbFLzoYGPfuWUMFPSaoA/join
Optimization Problems - Part 2 (3 Hour Video):
https://www.youtube.com/watch?v=7WUeqLyunVA
Calculus 1 Final Exam Review:
https://www.youtube.com/watch?v=WmBzmHru78w
published: 26 Apr 2021
-
Why Is Mathematical Optimization Such an Important Technology?
Senior Developer Dr. Roland Wunderling explains what is Mathematical Optimization and why it is such an important AI technology.
published: 11 Mar 2021
-
Dear all calculus students, This is why you're learning about optimization
Get free access to over 2500 documentaries on CuriosityStream: http://go.thoughtleaders.io/1621620200131 (use promo code "zachstar" at sign up)
STEMerch Store: https://stemerch.com/
Support the Channel: https://www.patreon.com/zachstar
PayPal(one time donation): https://www.paypal.me/ZachStarYT
►Follow me
Instagram: https://www.instagram.com/zachstar/
Twitter: https://twitter.com/ImZachStar
Full solution for 'lost fisherman problem': https://youtu.be/h60zE9QDkXo
Resources/Motivation for this video
The man who loved only numbers: https://amzn.to/37VlqWK
When least is best: https://amzn.to/3979h1l
Optical Ising Machine: https://spectrum.ieee.org/computing/hardware/to-crack-the-toughest-optimization-problems-just-add-lasers
Animations: Brainup Studios ( http://brainup.in/ )
►My Setup:
Sp...
published: 10 Feb 2020
-
How to Solve ANY Optimization Problem | Calculus 1
A step by step guide on solving optimization problems. We complete three examples of optimization problems, using calculus techniques to maximize volume given surface area, maximize area given perimeter, and to minimize distance on a curve from a given point. #calculus1 #apcalculus
2024 AP Calc AB FRQ Solutions: https://youtu.be/xRiUf-f7mDY
Finding Absolute Maximums and Minimums: https://youtu.be/sxxOl3nxkJ4
Maximize Volume of a Box: https://youtu.be/tHWiyQAmFJs
Minimize Distance from Point to Parabola: https://youtu.be/QQliWd0x_zk
Calculus 1 Course: https://www.youtube.com/playlist?list=PLztBpqftvzxWVDpl8oaz_Co6CW50KtGJy
Calculus 1 Exercises playlist: https://www.youtube.com/playlist?list=PLztBpqftvzxUEqGGgvL3EuIQUNcAdmVhx
◉Textbooks I Like◉
Graph Theory: https://amzn.to/3JHQtZj
Real ...
published: 08 Jul 2023
-
Solving Optimization Problems with Python Linear Programming
Want to solve complex linear programming problems faster?
Throw some Python at it!
Linear programming is a part of the field of mathematical programming and is a powerful way of solving complex combinatorial problems. It's used in manufacturing, resources, defence and transport quite extensively to improve outcomes and push performance.
Want to learn more? Well in this video, you'll learn:
- The three key parts to any linear programming problem
- How to formulate a manufacturing optimization problem
- To solve linear programming problems with docplex and Python!
Get the code: https://github.com/nicknochnack/LinearProgrammingBasics
Other Resources
Docplex Documentation: http://ibmdecisionoptimization.github.io/docplex-doc/
Oh, and don't forget to connect with me!
LinkedIn: https://www...
published: 17 Jun 2020
-
Boost Profitability and Efficiency with Mathematical Optimization
Abstract:
Hear findings from the Forrester Mathematical Optimization Financial Services Study and insights on how financial institutions can harness the power of MO to see significant benefits, such as the ability to increase profits and gain competitive advantage.
Speakers:
Dr. Ed Rothberg, CEO and Co-Founder at Gurobi Optimization
Mike Gualtieri, VP, Principal Analyst at Forrester
--
Learn more about Gurobi Optimization here: https://www.gurobi.com/
Check out our Optimization Application Demos here: https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,500 customers: https://www.gurobi.com/customers/example-customers/
--
Our Mission
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math progr...
published: 10 Dec 2021
-
Inference & GPU Optimization: AWQ
Join us as we explore cutting-edge techniques to optimize Large Language Models (LLMs) for inference! This event will dive into the tradeoffs between performance and cost in both LLMs and Small Language Models (SLMs). Learn how quantization, specifically Activate-aware Quantization (AWQ), compresses models while maintaining top-notch performance. We'll break down the findings from recent research and show you how to apply these techniques using Transformers. If you're interested in maximizing output while minimizing compute, this is an event you won't want to miss!
Event page: https://bit.ly/GPUOptimization
Have a question for a speaker? Drop them here:
https://app.sli.do/event/bArr6NPFLuhy1PRh69cTmy
Speakers:
Dr. Greg, Co-Founder & CEO AI Makerspace
https://www.linkedin.com/in/greglo...
published: 26 Sep 2024
-
Intro to Gradient Descent || Optimizing High-Dimensional Equations
Keep exploring at ► https://brilliant.org/TreforBazett. Get started for free for 30 days — and the first 200 people get 20% off an annual premium subscription!
How can we find maximums and minimums for complicated functions with an enormous number of variables like you might get when studying neural networks or machine learning? In this video we are going to talk about a topic from nonlinear optimization called Gradient Descent (or Gradient Ascent if you want maximums) where you step-by-step approach an extremum by stepping in the direction of the gradient vector. We're going to see the basic algorithm, see some common pitfalls, and then upgrade it using a method called line searches to improve the efficiency.
Check out my MATH MERCH line in collaboration with Beautiful Equations
►http...
published: 14 Feb 2023
11:35
What Is Mathematical Optimization?
A gentle and visual introduction to the topic of Convex Optimization. (1/3)
This video is the first of a series of three. The plan is as follows:
Part 1:...
A gentle and visual introduction to the topic of Convex Optimization. (1/3)
This video is the first of a series of three. The plan is as follows:
Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M)
Part 2: Convexity and the Principle of (Lagrangian) Duality (https://youtu.be/d0CF3d5aEGc)
Part 3: Algorithms for Convex Optimization (Interior Point Methods). (https://youtu.be/uh1Dk68cfWs)
-------------------------------
Typos:
- 8:34, The matrix A should be of size nxm, and the vector b should be of size 1xm.
--------------------------------
Timestamps:
0:00 Intro
2:50 What is optimization?
06:00 Linear programs
8:19 Linear regression
9:32 (Markovitz) Portfolio optimization
10:00 Conclusion
--------------------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/
This video would not have been possible without the help of Gökçe Dayanıklı.
--------------------------
Music
Sneaky Snitch by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/4384-sneaky-snitch
License: https://filmmusic.io/standard-license
Carefree by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/3476-carefree
License: https://filmmusic.io/standard-license
Funkorama by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/3788-funkorama
License: https://filmmusic.io/standard-license
https://wn.com/What_Is_Mathematical_Optimization
A gentle and visual introduction to the topic of Convex Optimization. (1/3)
This video is the first of a series of three. The plan is as follows:
Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M)
Part 2: Convexity and the Principle of (Lagrangian) Duality (https://youtu.be/d0CF3d5aEGc)
Part 3: Algorithms for Convex Optimization (Interior Point Methods). (https://youtu.be/uh1Dk68cfWs)
-------------------------------
Typos:
- 8:34, The matrix A should be of size nxm, and the vector b should be of size 1xm.
--------------------------------
Timestamps:
0:00 Intro
2:50 What is optimization?
06:00 Linear programs
8:19 Linear regression
9:32 (Markovitz) Portfolio optimization
10:00 Conclusion
--------------------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/
This video would not have been possible without the help of Gökçe Dayanıklı.
--------------------------
Music
Sneaky Snitch by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/4384-sneaky-snitch
License: https://filmmusic.io/standard-license
Carefree by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/3476-carefree
License: https://filmmusic.io/standard-license
Funkorama by Kevin MacLeod
Link: https://incompetech.filmmusic.io/song/3788-funkorama
License: https://filmmusic.io/standard-license
- published: 27 Jun 2021
- views: 139014
2:20
2: What is Mathematical Optimization?
--
Learn more about Gurobi Optimization here:
https://www.gurobi.com/
Check out our Optimization Application Demos here:
https://www.gurobi.com/resources/?cate...
--
Learn more about Gurobi Optimization here:
https://www.gurobi.com/
Check out our Optimization Application Demos here:
https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,400 customers:
https://www.gurobi.com/customers/example-customers/
--
About Gurobi
Gurobi produces the world’s fastest and most powerful mathematical optimization solver – the Gurobi Optimizer – which is used by leading global companies across more than 40 different industries to rapidly solve their complex, real-world problems and make automated decisions that optimize their efficiency and profitability.
As the market leader in mathematical optimization software, we aim to deliver not only the best solver, but also the best support – so that companies can fully leverage the power of mathematical optimization (on its own or in combination with other AI techniques such as machine learning) to drive optimal business decisions and outcomes.
Founded in 2008, Gurobi has operations across the USA, Europe, and Asia and more than 2,400 customers globally including Air France, Uber, and the National Football League (NFL). For more information, please visit https://www.gurobi.com/ or call +1 713 871 9341.
--
Like Us: https://www.facebook.com/GurobiOptimization/
Follow Us: https://twitter.com/gurobi
Connect with Us: https://www.linkedin.com/company/gurobi-optimization/
#optimization #datascience #dataanalytics #machinelearning #analytics #gurobipy #Gurobi #simplex #MIP #mixedintegerlinearprogramming #linearprogramming #mathematicaloptimization
https://wn.com/2_What_Is_Mathematical_Optimization
--
Learn more about Gurobi Optimization here:
https://www.gurobi.com/
Check out our Optimization Application Demos here:
https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,400 customers:
https://www.gurobi.com/customers/example-customers/
--
About Gurobi
Gurobi produces the world’s fastest and most powerful mathematical optimization solver – the Gurobi Optimizer – which is used by leading global companies across more than 40 different industries to rapidly solve their complex, real-world problems and make automated decisions that optimize their efficiency and profitability.
As the market leader in mathematical optimization software, we aim to deliver not only the best solver, but also the best support – so that companies can fully leverage the power of mathematical optimization (on its own or in combination with other AI techniques such as machine learning) to drive optimal business decisions and outcomes.
Founded in 2008, Gurobi has operations across the USA, Europe, and Asia and more than 2,400 customers globally including Air France, Uber, and the National Football League (NFL). For more information, please visit https://www.gurobi.com/ or call +1 713 871 9341.
--
Like Us: https://www.facebook.com/GurobiOptimization/
Follow Us: https://twitter.com/gurobi
Connect with Us: https://www.linkedin.com/company/gurobi-optimization/
#optimization #datascience #dataanalytics #machinelearning #analytics #gurobipy #Gurobi #simplex #MIP #mixedintegerlinearprogramming #linearprogramming #mathematicaloptimization
- published: 09 Apr 2020
- views: 5593
1:04:56
Optimization Problems - Calculus
This calculus video explains how to solve optimization problems. It explains how to solve the fence along the river problem, how to calculate the minimum dista...
This calculus video explains how to solve optimization problems. It explains how to solve the fence along the river problem, how to calculate the minimum distance between a point and a line, and how to maximize area while minimizing perimeter as in the case of fencing problems.
Get The Rest of the Video on Patreon:
https://www.patreon.com/MathScienceTutor
Direct Link To Part 2: Questions 13 thru 31:
https://bit.ly/2Y3Aem5
Derivative Applications - Free Formula Sheet:
https://bit.ly/4eV6r1b
______________________________
Join The Membership Program:
https://www.youtube.com/channel/UCEWpbFLzoYGPfuWUMFPSaoA/join
Optimization Problems - Part 2 (3 Hour Video):
https://www.youtube.com/watch?v=7WUeqLyunVA
Calculus 1 Final Exam Review:
https://www.youtube.com/watch?v=WmBzmHru78w
https://wn.com/Optimization_Problems_Calculus
This calculus video explains how to solve optimization problems. It explains how to solve the fence along the river problem, how to calculate the minimum distance between a point and a line, and how to maximize area while minimizing perimeter as in the case of fencing problems.
Get The Rest of the Video on Patreon:
https://www.patreon.com/MathScienceTutor
Direct Link To Part 2: Questions 13 thru 31:
https://bit.ly/2Y3Aem5
Derivative Applications - Free Formula Sheet:
https://bit.ly/4eV6r1b
______________________________
Join The Membership Program:
https://www.youtube.com/channel/UCEWpbFLzoYGPfuWUMFPSaoA/join
Optimization Problems - Part 2 (3 Hour Video):
https://www.youtube.com/watch?v=7WUeqLyunVA
Calculus 1 Final Exam Review:
https://www.youtube.com/watch?v=WmBzmHru78w
- published: 26 Apr 2021
- views: 1420189
2:41
Why Is Mathematical Optimization Such an Important Technology?
Senior Developer Dr. Roland Wunderling explains what is Mathematical Optimization and why it is such an important AI technology.
Senior Developer Dr. Roland Wunderling explains what is Mathematical Optimization and why it is such an important AI technology.
https://wn.com/Why_Is_Mathematical_Optimization_Such_An_Important_Technology
Senior Developer Dr. Roland Wunderling explains what is Mathematical Optimization and why it is such an important AI technology.
- published: 11 Mar 2021
- views: 1387
16:34
Dear all calculus students, This is why you're learning about optimization
Get free access to over 2500 documentaries on CuriosityStream: http://go.thoughtleaders.io/1621620200131 (use promo code "zachstar" at sign up)
STEMerch Store: ...
Get free access to over 2500 documentaries on CuriosityStream: http://go.thoughtleaders.io/1621620200131 (use promo code "zachstar" at sign up)
STEMerch Store: https://stemerch.com/
Support the Channel: https://www.patreon.com/zachstar
PayPal(one time donation): https://www.paypal.me/ZachStarYT
►Follow me
Instagram: https://www.instagram.com/zachstar/
Twitter: https://twitter.com/ImZachStar
Full solution for 'lost fisherman problem': https://youtu.be/h60zE9QDkXo
Resources/Motivation for this video
The man who loved only numbers: https://amzn.to/37VlqWK
When least is best: https://amzn.to/3979h1l
Optical Ising Machine: https://spectrum.ieee.org/computing/hardware/to-crack-the-toughest-optimization-problems-just-add-lasers
Animations: Brainup Studios ( http://brainup.in/ )
►My Setup:
Space Pictures: https://amzn.to/2CC4Kqj
Magnetic Floating Globe: https://amzn.to/2VgPdn0
Camera: https://amzn.to/2RivYu5
Mic: https://amzn.to/35bKiri
Tripod: https://amzn.to/2RgMTNL
Equilibrium Tube: https://amzn.to/2SowDrh
►Check out the my Amazon Store: https://www.amazon.com/shop/zachstar
https://wn.com/Dear_All_Calculus_Students,_This_Is_Why_You're_Learning_About_Optimization
Get free access to over 2500 documentaries on CuriosityStream: http://go.thoughtleaders.io/1621620200131 (use promo code "zachstar" at sign up)
STEMerch Store: https://stemerch.com/
Support the Channel: https://www.patreon.com/zachstar
PayPal(one time donation): https://www.paypal.me/ZachStarYT
►Follow me
Instagram: https://www.instagram.com/zachstar/
Twitter: https://twitter.com/ImZachStar
Full solution for 'lost fisherman problem': https://youtu.be/h60zE9QDkXo
Resources/Motivation for this video
The man who loved only numbers: https://amzn.to/37VlqWK
When least is best: https://amzn.to/3979h1l
Optical Ising Machine: https://spectrum.ieee.org/computing/hardware/to-crack-the-toughest-optimization-problems-just-add-lasers
Animations: Brainup Studios ( http://brainup.in/ )
►My Setup:
Space Pictures: https://amzn.to/2CC4Kqj
Magnetic Floating Globe: https://amzn.to/2VgPdn0
Camera: https://amzn.to/2RivYu5
Mic: https://amzn.to/35bKiri
Tripod: https://amzn.to/2RgMTNL
Equilibrium Tube: https://amzn.to/2SowDrh
►Check out the my Amazon Store: https://www.amazon.com/shop/zachstar
- published: 10 Feb 2020
- views: 560620
21:03
How to Solve ANY Optimization Problem | Calculus 1
A step by step guide on solving optimization problems. We complete three examples of optimization problems, using calculus techniques to maximize volume given s...
A step by step guide on solving optimization problems. We complete three examples of optimization problems, using calculus techniques to maximize volume given surface area, maximize area given perimeter, and to minimize distance on a curve from a given point. #calculus1 #apcalculus
2024 AP Calc AB FRQ Solutions: https://youtu.be/xRiUf-f7mDY
Finding Absolute Maximums and Minimums: https://youtu.be/sxxOl3nxkJ4
Maximize Volume of a Box: https://youtu.be/tHWiyQAmFJs
Minimize Distance from Point to Parabola: https://youtu.be/QQliWd0x_zk
Calculus 1 Course: https://www.youtube.com/playlist?list=PLztBpqftvzxWVDpl8oaz_Co6CW50KtGJy
Calculus 1 Exercises playlist: https://www.youtube.com/playlist?list=PLztBpqftvzxUEqGGgvL3EuIQUNcAdmVhx
◉Textbooks I Like◉
Graph Theory: https://amzn.to/3JHQtZj
Real Analysis: https://amzn.to/3CMdgjI
Abstract Algebra: https://amzn.to/3IjoZaO
Linear Algebra: https://amzn.to/43xAWEz
Calculus: https://amzn.to/3PieD1M
Proofs and Set Theory: https://amzn.to/367VBXP (available for free online)
Statistics: https://amzn.to/3tsaEER
Discrete Math: https://amzn.to/3qfhoUn
Number Theory: https://amzn.to/3JqpOQd
★DONATE★
◆ Support Wrath of Math on Patreon for early access to new videos and other exclusive benefits: https://www.patreon.com/join/wrathofmathlessons
◆ Donate on PayPal: https://www.paypal.me/wrathofmath
Thanks to Loke Tan, Matt Venia, Micheline, Doug Walker, Odd Hultberg, Marc, Roslyn Goddard, Shlome Ashkenazi, Barbora Sharrock, Mohamad Nossier, Rolf Waefler, Shadow Master, and James Mead for their generous support on Patreon!
Outro music is mine. You cannot find it anywhere, for now.
Follow Wrath of Math on...
● Instagram: https://www.instagram.com/wrathofmathedu
● Facebook: https://www.facebook.com/WrathofMath
● Twitter: https://twitter.com/wrathofmathedu
My Math Rap channel: https://www.youtube.com/channel/UCQ2UBhg5nwWCL2aPC7_IpDQ/featured
https://wn.com/How_To_Solve_Any_Optimization_Problem_|_Calculus_1
A step by step guide on solving optimization problems. We complete three examples of optimization problems, using calculus techniques to maximize volume given surface area, maximize area given perimeter, and to minimize distance on a curve from a given point. #calculus1 #apcalculus
2024 AP Calc AB FRQ Solutions: https://youtu.be/xRiUf-f7mDY
Finding Absolute Maximums and Minimums: https://youtu.be/sxxOl3nxkJ4
Maximize Volume of a Box: https://youtu.be/tHWiyQAmFJs
Minimize Distance from Point to Parabola: https://youtu.be/QQliWd0x_zk
Calculus 1 Course: https://www.youtube.com/playlist?list=PLztBpqftvzxWVDpl8oaz_Co6CW50KtGJy
Calculus 1 Exercises playlist: https://www.youtube.com/playlist?list=PLztBpqftvzxUEqGGgvL3EuIQUNcAdmVhx
◉Textbooks I Like◉
Graph Theory: https://amzn.to/3JHQtZj
Real Analysis: https://amzn.to/3CMdgjI
Abstract Algebra: https://amzn.to/3IjoZaO
Linear Algebra: https://amzn.to/43xAWEz
Calculus: https://amzn.to/3PieD1M
Proofs and Set Theory: https://amzn.to/367VBXP (available for free online)
Statistics: https://amzn.to/3tsaEER
Discrete Math: https://amzn.to/3qfhoUn
Number Theory: https://amzn.to/3JqpOQd
★DONATE★
◆ Support Wrath of Math on Patreon for early access to new videos and other exclusive benefits: https://www.patreon.com/join/wrathofmathlessons
◆ Donate on PayPal: https://www.paypal.me/wrathofmath
Thanks to Loke Tan, Matt Venia, Micheline, Doug Walker, Odd Hultberg, Marc, Roslyn Goddard, Shlome Ashkenazi, Barbora Sharrock, Mohamad Nossier, Rolf Waefler, Shadow Master, and James Mead for their generous support on Patreon!
Outro music is mine. You cannot find it anywhere, for now.
Follow Wrath of Math on...
● Instagram: https://www.instagram.com/wrathofmathedu
● Facebook: https://www.facebook.com/WrathofMath
● Twitter: https://twitter.com/wrathofmathedu
My Math Rap channel: https://www.youtube.com/channel/UCQ2UBhg5nwWCL2aPC7_IpDQ/featured
- published: 08 Jul 2023
- views: 55577
9:49
Solving Optimization Problems with Python Linear Programming
Want to solve complex linear programming problems faster?
Throw some Python at it!
Linear programming is a part of the field of mathematical programming and i...
Want to solve complex linear programming problems faster?
Throw some Python at it!
Linear programming is a part of the field of mathematical programming and is a powerful way of solving complex combinatorial problems. It's used in manufacturing, resources, defence and transport quite extensively to improve outcomes and push performance.
Want to learn more? Well in this video, you'll learn:
- The three key parts to any linear programming problem
- How to formulate a manufacturing optimization problem
- To solve linear programming problems with docplex and Python!
Get the code: https://github.com/nicknochnack/LinearProgrammingBasics
Other Resources
Docplex Documentation: http://ibmdecisionoptimization.github.io/docplex-doc/
Oh, and don't forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasrenotte/
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack
Happy coding!
Nick
https://wn.com/Solving_Optimization_Problems_With_Python_Linear_Programming
Want to solve complex linear programming problems faster?
Throw some Python at it!
Linear programming is a part of the field of mathematical programming and is a powerful way of solving complex combinatorial problems. It's used in manufacturing, resources, defence and transport quite extensively to improve outcomes and push performance.
Want to learn more? Well in this video, you'll learn:
- The three key parts to any linear programming problem
- How to formulate a manufacturing optimization problem
- To solve linear programming problems with docplex and Python!
Get the code: https://github.com/nicknochnack/LinearProgrammingBasics
Other Resources
Docplex Documentation: http://ibmdecisionoptimization.github.io/docplex-doc/
Oh, and don't forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasrenotte/
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack
Happy coding!
Nick
- published: 17 Jun 2020
- views: 88737
42:46
Boost Profitability and Efficiency with Mathematical Optimization
Abstract:
Hear findings from the Forrester Mathematical Optimization Financial Services Study and insights on how financial institutions can harness the power o...
Abstract:
Hear findings from the Forrester Mathematical Optimization Financial Services Study and insights on how financial institutions can harness the power of MO to see significant benefits, such as the ability to increase profits and gain competitive advantage.
Speakers:
Dr. Ed Rothberg, CEO and Co-Founder at Gurobi Optimization
Mike Gualtieri, VP, Principal Analyst at Forrester
--
Learn more about Gurobi Optimization here: https://www.gurobi.com/
Check out our Optimization Application Demos here: https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,500 customers: https://www.gurobi.com/customers/example-customers/
--
Our Mission
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease.
Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,500 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing.
--
Like Us: https://www.facebook.com/GurobiOptimization/
Follow Us: https://twitter.com/gurobi
Connect with Us: https://www.linkedin.com/company/gurobi-optimization/
#optimization #datascience #dataanalytics #machinelearning #analytics
https://wn.com/Boost_Profitability_And_Efficiency_With_Mathematical_Optimization
Abstract:
Hear findings from the Forrester Mathematical Optimization Financial Services Study and insights on how financial institutions can harness the power of MO to see significant benefits, such as the ability to increase profits and gain competitive advantage.
Speakers:
Dr. Ed Rothberg, CEO and Co-Founder at Gurobi Optimization
Mike Gualtieri, VP, Principal Analyst at Forrester
--
Learn more about Gurobi Optimization here: https://www.gurobi.com/
Check out our Optimization Application Demos here: https://www.gurobi.com/resources/?category-filter=demos
Check out our 2,500 customers: https://www.gurobi.com/customers/example-customers/
--
Our Mission
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease.
Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,500 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing.
--
Like Us: https://www.facebook.com/GurobiOptimization/
Follow Us: https://twitter.com/gurobi
Connect with Us: https://www.linkedin.com/company/gurobi-optimization/
#optimization #datascience #dataanalytics #machinelearning #analytics
- published: 10 Dec 2021
- views: 1561
59:53
Inference & GPU Optimization: AWQ
Join us as we explore cutting-edge techniques to optimize Large Language Models (LLMs) for inference! This event will dive into the tradeoffs between performanc...
Join us as we explore cutting-edge techniques to optimize Large Language Models (LLMs) for inference! This event will dive into the tradeoffs between performance and cost in both LLMs and Small Language Models (SLMs). Learn how quantization, specifically Activate-aware Quantization (AWQ), compresses models while maintaining top-notch performance. We'll break down the findings from recent research and show you how to apply these techniques using Transformers. If you're interested in maximizing output while minimizing compute, this is an event you won't want to miss!
Event page: https://bit.ly/GPUOptimization
Have a question for a speaker? Drop them here:
https://app.sli.do/event/bArr6NPFLuhy1PRh69cTmy
Speakers:
Dr. Greg, Co-Founder & CEO AI Makerspace
https://www.linkedin.com/in/gregloughane
The Wiz, Co-Founder & CTO AI Makerspace
https://www.linkedin.com/in/csalexiuk/
Apply for our new AI Engineering Bootcamp on Maven today!
https://bit.ly/aie1
For team leaders, check out!
https://aimakerspace.io/gen-ai-upskilling-for-teams/
Join our community to start building, shipping, and sharing with us today!
https://discord.gg/RzhvYvAwzA
How'd we do? Share your feedback and suggestions for future events.
https://forms.gle/ZTebEuDCY1n8J8gh9
https://wn.com/Inference_Gpu_Optimization_Awq
Join us as we explore cutting-edge techniques to optimize Large Language Models (LLMs) for inference! This event will dive into the tradeoffs between performance and cost in both LLMs and Small Language Models (SLMs). Learn how quantization, specifically Activate-aware Quantization (AWQ), compresses models while maintaining top-notch performance. We'll break down the findings from recent research and show you how to apply these techniques using Transformers. If you're interested in maximizing output while minimizing compute, this is an event you won't want to miss!
Event page: https://bit.ly/GPUOptimization
Have a question for a speaker? Drop them here:
https://app.sli.do/event/bArr6NPFLuhy1PRh69cTmy
Speakers:
Dr. Greg, Co-Founder & CEO AI Makerspace
https://www.linkedin.com/in/gregloughane
The Wiz, Co-Founder & CTO AI Makerspace
https://www.linkedin.com/in/csalexiuk/
Apply for our new AI Engineering Bootcamp on Maven today!
https://bit.ly/aie1
For team leaders, check out!
https://aimakerspace.io/gen-ai-upskilling-for-teams/
Join our community to start building, shipping, and sharing with us today!
https://discord.gg/RzhvYvAwzA
How'd we do? Share your feedback and suggestions for future events.
https://forms.gle/ZTebEuDCY1n8J8gh9
- published: 26 Sep 2024
- views: 514
11:04
Intro to Gradient Descent || Optimizing High-Dimensional Equations
Keep exploring at ► https://brilliant.org/TreforBazett. Get started for free for 30 days — and the first 200 people get 20% off an annual premium subscription!
...
Keep exploring at ► https://brilliant.org/TreforBazett. Get started for free for 30 days — and the first 200 people get 20% off an annual premium subscription!
How can we find maximums and minimums for complicated functions with an enormous number of variables like you might get when studying neural networks or machine learning? In this video we are going to talk about a topic from nonlinear optimization called Gradient Descent (or Gradient Ascent if you want maximums) where you step-by-step approach an extremum by stepping in the direction of the gradient vector. We're going to see the basic algorithm, see some common pitfalls, and then upgrade it using a method called line searches to improve the efficiency.
Check out my MATH MERCH line in collaboration with Beautiful Equations
►https://beautifulequations.net/pages/trefor
COURSE PLAYLISTS:
►DISCRETE MATH: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxersk8fUxiUMSIx0DBqsKZS
►LINEAR ALGEBRA: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfUl0tcqPNTJsb7R6BqSLo6
►CALCULUS I: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfT9RMcReZ4WcoVILP4k6-m
► CALCULUS II: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxc4ySKTIW19TLrT91Ik9M4n
►MULTIVARIABLE CALCULUS (Calc III): https://www.youtube.com/playlist?list=PLHXZ9OQGMqxc_CvEy7xBKRQr6I214QJcd
►VECTOR CALCULUS (Calc IV) https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfW0GMqeUE1bLKaYor6kbHa
►DIFFERENTIAL EQUATIONS: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxde-SlgmWlCmNHroIWtujBw
►LAPLACE TRANSFORM: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxcJXnLr08cyNaup4RDsbAl1
►GAME THEORY: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxdzD8KpTHz6_gsw9pPxRFlX
OTHER PLAYLISTS:
► Learning Math Series
https://www.youtube.com/watch?v=LPH2lqis3D0&list=PLHXZ9OQGMqxfSkRtlL5KPq6JqMNTh_MBw
►Cool Math Series:
https://www.youtube.com/playlist?list=PLHXZ9OQGMqxelE_9RzwJ-cqfUtaFBpiho
BECOME A MEMBER:
►Join: https://www.youtube.com/channel/UC9rTsvTxJnx1DNrDA3Rqa6A/join
MATH BOOKS I LOVE (affilliate link):
► https://www.amazon.com/shop/treforbazett
SOCIALS:
►Twitter (math based): http://twitter.com/treforbazett
►Instagram (photography based): http://instagram.com/treforphotography
https://wn.com/Intro_To_Gradient_Descent_||_Optimizing_High_Dimensional_Equations
Keep exploring at ► https://brilliant.org/TreforBazett. Get started for free for 30 days — and the first 200 people get 20% off an annual premium subscription!
How can we find maximums and minimums for complicated functions with an enormous number of variables like you might get when studying neural networks or machine learning? In this video we are going to talk about a topic from nonlinear optimization called Gradient Descent (or Gradient Ascent if you want maximums) where you step-by-step approach an extremum by stepping in the direction of the gradient vector. We're going to see the basic algorithm, see some common pitfalls, and then upgrade it using a method called line searches to improve the efficiency.
Check out my MATH MERCH line in collaboration with Beautiful Equations
►https://beautifulequations.net/pages/trefor
COURSE PLAYLISTS:
►DISCRETE MATH: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxersk8fUxiUMSIx0DBqsKZS
►LINEAR ALGEBRA: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfUl0tcqPNTJsb7R6BqSLo6
►CALCULUS I: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfT9RMcReZ4WcoVILP4k6-m
► CALCULUS II: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxc4ySKTIW19TLrT91Ik9M4n
►MULTIVARIABLE CALCULUS (Calc III): https://www.youtube.com/playlist?list=PLHXZ9OQGMqxc_CvEy7xBKRQr6I214QJcd
►VECTOR CALCULUS (Calc IV) https://www.youtube.com/playlist?list=PLHXZ9OQGMqxfW0GMqeUE1bLKaYor6kbHa
►DIFFERENTIAL EQUATIONS: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxde-SlgmWlCmNHroIWtujBw
►LAPLACE TRANSFORM: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxcJXnLr08cyNaup4RDsbAl1
►GAME THEORY: https://www.youtube.com/playlist?list=PLHXZ9OQGMqxdzD8KpTHz6_gsw9pPxRFlX
OTHER PLAYLISTS:
► Learning Math Series
https://www.youtube.com/watch?v=LPH2lqis3D0&list=PLHXZ9OQGMqxfSkRtlL5KPq6JqMNTh_MBw
►Cool Math Series:
https://www.youtube.com/playlist?list=PLHXZ9OQGMqxelE_9RzwJ-cqfUtaFBpiho
BECOME A MEMBER:
►Join: https://www.youtube.com/channel/UC9rTsvTxJnx1DNrDA3Rqa6A/join
MATH BOOKS I LOVE (affilliate link):
► https://www.amazon.com/shop/treforbazett
SOCIALS:
►Twitter (math based): http://twitter.com/treforbazett
►Instagram (photography based): http://instagram.com/treforphotography
- published: 14 Feb 2023
- views: 68853
-
Programming with Math | The Lambda Calculus
The Lambda Calculus is a tiny mathematical programming language that has the same computational power as any language you can dream of. In this video, we'll first explore this calculus before seeing how we can flesh it out into a functional programming language.
After a brief tour of a simple type system, we'll see why the Lambda Calculus has some surprising applications in the field of mathematical logic, and how the implications of this relationship could alter the way that we study mathematics forever.
― Timestamps ―
0:00 - Intro
0:42 - Definition
5:30 - Multiple Inputs
8:10 - Booleans and Conditionals
13:11 - Simple Types
16:32 - Curry-Howard Correspondence
20:58 - Outro
― Credits ―
All animation and voiceover created by Eyesomorphic.
Lean4 proof of infinitude of primes taken from...
published: 14 Jun 2024
-
why you NEED math for programming
Get the JomaClass membership: https://joma.tech/dsa
First 100 people get 15% off the yearly subscription with promo code "DONUT"
Donut C article by Andy Sloane:
https://www.a1k0n.net/2011/07/20/donut-math.html
Music by Joy Ngiaw:
https://www.joyngiaw.com/
https://www.instagram.com/joyngiaw/
📱 SOCIAL MEDIA
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
https://www.instagram.com/jomakaze/
https://twitter.com/jomakaze
https://www.facebook.com/jomakaze
published: 06 Jan 2021
-
10 Math Concepts for Programmers
Learn 10 essential math concepts for software engineering and technical interviews. Understand how programmers use mathematics in fields like AI, game dev, crypto, machine learning, and more.
#math #programming #top10
💬 Chat with Me on Discord
https://discord.gg/fireship
🔗 Resources
Computer Science 101 https://youtu.be/-uleG_Vecis
Cryptography for Developers https://youtu.be/NuyzuNBFWxQ
Technical Interview Prep https://youtu.be/1t1_a1BZ04o
📚 Chapters
🔥 Get More Content - Upgrade to PRO
Upgrade at https://fireship.io/pro
Use code YT25 for 25% off PRO access
🎨 My Editor Settings
- Atom One Dark
- vscode-icons
- Fira Code Font
🔖 Topics Covered
- Do programmers need math?
- Math tutorial for programming
- Machine learning math
- Do computer hackers use math?
- Linear algebra for...
published: 21 Apr 2023
-
The Art of Linear Programming
A visual-heavy introduction to Linear Programming including basic definitions, solution via the Simplex method, the principle of duality and Integer Linear Programming. #some3
More problems: https://slama.dev/youtube/linear-programming-in-python/
Made as my entry to SoME3: https://3blue1brown.substack.com/p/some3-begins
------------------
Timetable:
0:00 - Introduction
0:26 - Basics
3:44 - Simplex Method
11:47 - Duality
14:01 - Integer Linear Programming
17:31 - Conclusion
------------------
Source code: https://github.com/xiaoxiae/videos/tree/master/18-lopt/
Music (in the order it appears in the video):
► Cases to Rest by Blue Dot Sessions: https://app.sessions.blue/browse/track/139762
► Thannoid by Blue Dot Sessions: https://app.sessions.blue/browse/track/126782
► ZigZag Heart by ...
published: 04 Jul 2023
-
Laboratory of Applied Mathematical Programming and Statistics
Departamento de Engenharia Elétrica e Industrial PUC-Rio
published: 04 Nov 2022
-
Mathematical Programming | Lê Nguyên Hoang
This video defines what a mathematical program is.
Speaker and edition: Lê Nguyên Hoang
published: 16 Aug 2017
-
5 Math Skills Every Programmer Needs
Do you need math to become a programmer? Are Software Engineers good at Math? If yes, how much Math do you need to learn coding? I will answer these questions today.
► For more content like this, subscribe to our channel: https://www.youtube.com/PowerCouple26
► Follow us on Linkedin:
https://www.linkedin.com/in/gabag26
https://www.linkedin.com/in/sarrabounouh
► Let's be FRIENDS! https://www.instagram.com/power_couple26/
► For business inquiries, reach us on: [email protected]
#coding #maths #softwareengineering
published: 22 Jan 2023
-
The Man Who Revolutionized Computer Science With Math
Leslie Lamport revolutionized how computers talk to each other. The Turing Award-winning computer scientist pioneered the field of distributed systems, where multiple components on different networks coordinate to achieve a common objective. (Internet searches, cloud computing and artificial intelligence all involve orchestrating legions of powerful computing machines to work together.) In the early 1980s, Lamport also created LaTeX, a document preparation system that provides sophisticated ways to typeset complex formulas and format scientific documents. In 1989, Lamport invented Paxos, a “consensus algorithm” that allows multiple computers to execute complex tasks; without it, modern computing could not exist. He’s also brought more attention to a handful of problems, giving them distinc...
published: 17 May 2022
-
What is Feature Engineering? #100daysofai
Day 6 of #100DaysOfAI: Feature Engineering
Following up on yesterday’s discussion on data preparation, today we delve into feature engineering—transforming prepared data into actionable business intelligence. By creating meaningful features and optimizing existing ones, you can significantly enhance the performance of your AI models. Here are some essential considerations for effective feature engineering in an enterprise context:
1. Creating New Features:
- Derived Features: Use existing data to create new features. For instance, calculate "customer tenure" by subtracting the signup date from the current date.
- Domain Knowledge: Leverage your industry knowledge to create features that capture essential aspects of your business. For example, features like "average purchase value" or "p...
published: 22 Jun 2024
-
Simplex Method Problem 1- Linear Programming Problems (LPP) - Engineering Mathematics - 4
Subject - Engineering Mathematics - 4
Video Name -Simplex Method Problem 1
Chapter - Linear Programming Problems (LPP)
Faculty - Prof. Farhan Meer
Upskill and get Placements with Ekeeda Career Tracks
Data Science - https://ekeeda.com/career-track/data-scientist
Software Development Engineer - https://ekeeda.com/career-track/software-development-engineer
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Get FREE Trial for GATE 2023 Exam with Ekeeda GATE - 20000+ Lectures & Notes, strategy, updates, and notifications which will help you to crack your GATE exam.
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Get Free Notes of All Engineering Subjects & Technology
https://ekeeda.com/digital-library
Access the Complete Playlist of Subje...
published: 08 Feb 2021
21:48
Programming with Math | The Lambda Calculus
The Lambda Calculus is a tiny mathematical programming language that has the same computational power as any language you can dream of. In this video, we'll fir...
The Lambda Calculus is a tiny mathematical programming language that has the same computational power as any language you can dream of. In this video, we'll first explore this calculus before seeing how we can flesh it out into a functional programming language.
After a brief tour of a simple type system, we'll see why the Lambda Calculus has some surprising applications in the field of mathematical logic, and how the implications of this relationship could alter the way that we study mathematics forever.
― Timestamps ―
0:00 - Intro
0:42 - Definition
5:30 - Multiple Inputs
8:10 - Booleans and Conditionals
13:11 - Simple Types
16:32 - Curry-Howard Correspondence
20:58 - Outro
― Credits ―
All animation and voiceover created by Eyesomorphic.
Lean4 proof of infinitude of primes taken from mathlib4 under Apache 2.0 license: https://github.com/leanprover-community/mathlib/blob/master/src%2Fdata%2Fnat%2Fprime.lean
Background music: 'Reminisce', composed by Caleb Peppiatt.
― Further Reading ―
Types and Programming Languages, by Benjamin C. Pierce (Book)
Category Theory and Why We Care, by Eyesomorphic (Lecture series): https://www.youtube.com/playlist?list=PLoCKNPo3VR0I2wqT2wemCNIlpjdy_Ry_q
― Corrections ―
At 4:35, the word 'comptuter' should obviously be 'computer', sorry about that!
An entry to #SoMEPi
https://wn.com/Programming_With_Math_|_The_Lambda_Calculus
The Lambda Calculus is a tiny mathematical programming language that has the same computational power as any language you can dream of. In this video, we'll first explore this calculus before seeing how we can flesh it out into a functional programming language.
After a brief tour of a simple type system, we'll see why the Lambda Calculus has some surprising applications in the field of mathematical logic, and how the implications of this relationship could alter the way that we study mathematics forever.
― Timestamps ―
0:00 - Intro
0:42 - Definition
5:30 - Multiple Inputs
8:10 - Booleans and Conditionals
13:11 - Simple Types
16:32 - Curry-Howard Correspondence
20:58 - Outro
― Credits ―
All animation and voiceover created by Eyesomorphic.
Lean4 proof of infinitude of primes taken from mathlib4 under Apache 2.0 license: https://github.com/leanprover-community/mathlib/blob/master/src%2Fdata%2Fnat%2Fprime.lean
Background music: 'Reminisce', composed by Caleb Peppiatt.
― Further Reading ―
Types and Programming Languages, by Benjamin C. Pierce (Book)
Category Theory and Why We Care, by Eyesomorphic (Lecture series): https://www.youtube.com/playlist?list=PLoCKNPo3VR0I2wqT2wemCNIlpjdy_Ry_q
― Corrections ―
At 4:35, the word 'comptuter' should obviously be 'computer', sorry about that!
An entry to #SoMEPi
- published: 14 Jun 2024
- views: 232237
5:03
why you NEED math for programming
Get the JomaClass membership: https://joma.tech/dsa
First 100 people get 15% off the yearly subscription with promo code "DONUT"
Donut C article by Andy Sloane...
Get the JomaClass membership: https://joma.tech/dsa
First 100 people get 15% off the yearly subscription with promo code "DONUT"
Donut C article by Andy Sloane:
https://www.a1k0n.net/2011/07/20/donut-math.html
Music by Joy Ngiaw:
https://www.joyngiaw.com/
https://www.instagram.com/joyngiaw/
📱 SOCIAL MEDIA
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
https://www.instagram.com/jomakaze/
https://twitter.com/jomakaze
https://www.facebook.com/jomakaze
https://wn.com/Why_You_Need_Math_For_Programming
Get the JomaClass membership: https://joma.tech/dsa
First 100 people get 15% off the yearly subscription with promo code "DONUT"
Donut C article by Andy Sloane:
https://www.a1k0n.net/2011/07/20/donut-math.html
Music by Joy Ngiaw:
https://www.joyngiaw.com/
https://www.instagram.com/joyngiaw/
📱 SOCIAL MEDIA
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
https://www.instagram.com/jomakaze/
https://twitter.com/jomakaze
https://www.facebook.com/jomakaze
- published: 06 Jan 2021
- views: 8756474
9:32
10 Math Concepts for Programmers
Learn 10 essential math concepts for software engineering and technical interviews. Understand how programmers use mathematics in fields like AI, game dev, cryp...
Learn 10 essential math concepts for software engineering and technical interviews. Understand how programmers use mathematics in fields like AI, game dev, crypto, machine learning, and more.
#math #programming #top10
💬 Chat with Me on Discord
https://discord.gg/fireship
🔗 Resources
Computer Science 101 https://youtu.be/-uleG_Vecis
Cryptography for Developers https://youtu.be/NuyzuNBFWxQ
Technical Interview Prep https://youtu.be/1t1_a1BZ04o
📚 Chapters
🔥 Get More Content - Upgrade to PRO
Upgrade at https://fireship.io/pro
Use code YT25 for 25% off PRO access
🎨 My Editor Settings
- Atom One Dark
- vscode-icons
- Fira Code Font
🔖 Topics Covered
- Do programmers need math?
- Math tutorial for programming
- Machine learning math
- Do computer hackers use math?
- Linear algebra for programmers
- Boolean Algebra explained
- Combinatorics explained
- How does Big-O notation work
https://wn.com/10_Math_Concepts_For_Programmers
Learn 10 essential math concepts for software engineering and technical interviews. Understand how programmers use mathematics in fields like AI, game dev, crypto, machine learning, and more.
#math #programming #top10
💬 Chat with Me on Discord
https://discord.gg/fireship
🔗 Resources
Computer Science 101 https://youtu.be/-uleG_Vecis
Cryptography for Developers https://youtu.be/NuyzuNBFWxQ
Technical Interview Prep https://youtu.be/1t1_a1BZ04o
📚 Chapters
🔥 Get More Content - Upgrade to PRO
Upgrade at https://fireship.io/pro
Use code YT25 for 25% off PRO access
🎨 My Editor Settings
- Atom One Dark
- vscode-icons
- Fira Code Font
🔖 Topics Covered
- Do programmers need math?
- Math tutorial for programming
- Machine learning math
- Do computer hackers use math?
- Linear algebra for programmers
- Boolean Algebra explained
- Combinatorics explained
- How does Big-O notation work
- published: 21 Apr 2023
- views: 1935344
18:56
The Art of Linear Programming
A visual-heavy introduction to Linear Programming including basic definitions, solution via the Simplex method, the principle of duality and Integer Linear Prog...
A visual-heavy introduction to Linear Programming including basic definitions, solution via the Simplex method, the principle of duality and Integer Linear Programming. #some3
More problems: https://slama.dev/youtube/linear-programming-in-python/
Made as my entry to SoME3: https://3blue1brown.substack.com/p/some3-begins
------------------
Timetable:
0:00 - Introduction
0:26 - Basics
3:44 - Simplex Method
11:47 - Duality
14:01 - Integer Linear Programming
17:31 - Conclusion
------------------
Source code: https://github.com/xiaoxiae/videos/tree/master/18-lopt/
Music (in the order it appears in the video):
► Cases to Rest by Blue Dot Sessions: https://app.sessions.blue/browse/track/139762
► Thannoid by Blue Dot Sessions: https://app.sessions.blue/browse/track/126782
► ZigZag Heart by Blue Dot Sessions: https://app.sessions.blue/browse/track/31462
► Maisie Dreamer by Blue Dot Sessions: https://app.sessions.blue/browse/track/31458
► Night Light by Blue Dot Sessions: https://app.sessions.blue/browse/track/189819
Software used:
► Manim (animation software): https://github.com/ManimCommunity/manim/
► Kdenlive (video cutting): https://kdenlive.org/en/
► ffmpeg (audio/video processing): https://ffmpeg.org/
► OBS (audio/video recording): https://obsproject.com/download
► arecord (audio recording): https://linux.die.net/man/1/arecord
► sox (audio processing): http://sox.sourceforge.net/
► Inkscape (vector image editing): https://inkscape.org/
► Midjourney (image generation): https://www.midjourney.com/app/
Social media:
► Website (for other things I'm up to): https://slama.dev/
► Patreon (if you'd like to support me): https://www.patreon.com/YTomS
Thanks to Matěj Kripner, Martin Balko, Lucia Zhang, Václav Rozhoň (@polylog), Kateřina Sulková, Mohit Shrestha, Teo Tuicu and Tomáš Sláma (my dad, not me) for valuable feedback.
------------------
[EN] Gerard Sierksma; Yori Zwols (2015). Linear and Integer Optimization: Theory and Practice
https://www.taylorfrancis.com/books/mono/10.1201/b18378/linear-integer-optimization-gerard-sierksma-gerard-sierksma-yori-zwols
[CZ] Přednáška Jiřího Sgalla: Lineární programování a kombinatorická optimalizace
https://iuuk.mff.cuni.cz/~sgall/vyuka/LP/
[EN] George B. Dantzig (1982): Reminiscences about the origins of linear programming
https://apps.dtic.mil/sti/pdfs/ADA112060.pdf
https://wn.com/The_Art_Of_Linear_Programming
A visual-heavy introduction to Linear Programming including basic definitions, solution via the Simplex method, the principle of duality and Integer Linear Programming. #some3
More problems: https://slama.dev/youtube/linear-programming-in-python/
Made as my entry to SoME3: https://3blue1brown.substack.com/p/some3-begins
------------------
Timetable:
0:00 - Introduction
0:26 - Basics
3:44 - Simplex Method
11:47 - Duality
14:01 - Integer Linear Programming
17:31 - Conclusion
------------------
Source code: https://github.com/xiaoxiae/videos/tree/master/18-lopt/
Music (in the order it appears in the video):
► Cases to Rest by Blue Dot Sessions: https://app.sessions.blue/browse/track/139762
► Thannoid by Blue Dot Sessions: https://app.sessions.blue/browse/track/126782
► ZigZag Heart by Blue Dot Sessions: https://app.sessions.blue/browse/track/31462
► Maisie Dreamer by Blue Dot Sessions: https://app.sessions.blue/browse/track/31458
► Night Light by Blue Dot Sessions: https://app.sessions.blue/browse/track/189819
Software used:
► Manim (animation software): https://github.com/ManimCommunity/manim/
► Kdenlive (video cutting): https://kdenlive.org/en/
► ffmpeg (audio/video processing): https://ffmpeg.org/
► OBS (audio/video recording): https://obsproject.com/download
► arecord (audio recording): https://linux.die.net/man/1/arecord
► sox (audio processing): http://sox.sourceforge.net/
► Inkscape (vector image editing): https://inkscape.org/
► Midjourney (image generation): https://www.midjourney.com/app/
Social media:
► Website (for other things I'm up to): https://slama.dev/
► Patreon (if you'd like to support me): https://www.patreon.com/YTomS
Thanks to Matěj Kripner, Martin Balko, Lucia Zhang, Václav Rozhoň (@polylog), Kateřina Sulková, Mohit Shrestha, Teo Tuicu and Tomáš Sláma (my dad, not me) for valuable feedback.
------------------
[EN] Gerard Sierksma; Yori Zwols (2015). Linear and Integer Optimization: Theory and Practice
https://www.taylorfrancis.com/books/mono/10.1201/b18378/linear-integer-optimization-gerard-sierksma-gerard-sierksma-yori-zwols
[CZ] Přednáška Jiřího Sgalla: Lineární programování a kombinatorická optimalizace
https://iuuk.mff.cuni.cz/~sgall/vyuka/LP/
[EN] George B. Dantzig (1982): Reminiscences about the origins of linear programming
https://apps.dtic.mil/sti/pdfs/ADA112060.pdf
- published: 04 Jul 2023
- views: 716529
2:53
Mathematical Programming | Lê Nguyên Hoang
This video defines what a mathematical program is.
Speaker and edition: Lê Nguyên Hoang
This video defines what a mathematical program is.
Speaker and edition: Lê Nguyên Hoang
https://wn.com/Mathematical_Programming_|_Lê_Nguyên_Hoang
This video defines what a mathematical program is.
Speaker and edition: Lê Nguyên Hoang
- published: 16 Aug 2017
- views: 1664
9:08
5 Math Skills Every Programmer Needs
Do you need math to become a programmer? Are Software Engineers good at Math? If yes, how much Math do you need to learn coding? I will answer these questions t...
Do you need math to become a programmer? Are Software Engineers good at Math? If yes, how much Math do you need to learn coding? I will answer these questions today.
► For more content like this, subscribe to our channel: https://www.youtube.com/PowerCouple26
► Follow us on Linkedin:
https://www.linkedin.com/in/gabag26
https://www.linkedin.com/in/sarrabounouh
► Let's be FRIENDS! https://www.instagram.com/power_couple26/
► For business inquiries, reach us on:
[email protected]
#coding #maths #softwareengineering
https://wn.com/5_Math_Skills_Every_Programmer_Needs
Do you need math to become a programmer? Are Software Engineers good at Math? If yes, how much Math do you need to learn coding? I will answer these questions today.
► For more content like this, subscribe to our channel: https://www.youtube.com/PowerCouple26
► Follow us on Linkedin:
https://www.linkedin.com/in/gabag26
https://www.linkedin.com/in/sarrabounouh
► Let's be FRIENDS! https://www.instagram.com/power_couple26/
► For business inquiries, reach us on:
[email protected]
#coding #maths #softwareengineering
- published: 22 Jan 2023
- views: 1069993
7:50
The Man Who Revolutionized Computer Science With Math
Leslie Lamport revolutionized how computers talk to each other. The Turing Award-winning computer scientist pioneered the field of distributed systems, where mu...
Leslie Lamport revolutionized how computers talk to each other. The Turing Award-winning computer scientist pioneered the field of distributed systems, where multiple components on different networks coordinate to achieve a common objective. (Internet searches, cloud computing and artificial intelligence all involve orchestrating legions of powerful computing machines to work together.) In the early 1980s, Lamport also created LaTeX, a document preparation system that provides sophisticated ways to typeset complex formulas and format scientific documents. In 1989, Lamport invented Paxos, a “consensus algorithm” that allows multiple computers to execute complex tasks; without it, modern computing could not exist. He’s also brought more attention to a handful of problems, giving them distinctive names like the bakery algorithm and the Byzantine Generals Problem. Lamport’s work since the 1990s has focused on “formal verification,” the use of mathematical proofs to verify the correctness of software and hardware systems. Notably, he created a “specification language” called TLA+ (for Temporal Logic of Actions), which employs the precise language of mathematics to prevent bugs and avoid design flaws.
Read more at Quanta Magazine: https://www.quantamagazine.org/bringing-mathematical-perfection-to-software-20220516/
- VISIT our Website: https://www.quantamagazine.org
- LIKE us on Facebook: https://www.facebook.com/QuantaNews
- FOLLOW us Twitter: https://twitter.com/QuantaMagazine
Quanta Magazine is an editorially independent publication supported by the Simons Foundation https://www.simonsfoundation.org/
#computerscience #math
https://wn.com/The_Man_Who_Revolutionized_Computer_Science_With_Math
Leslie Lamport revolutionized how computers talk to each other. The Turing Award-winning computer scientist pioneered the field of distributed systems, where multiple components on different networks coordinate to achieve a common objective. (Internet searches, cloud computing and artificial intelligence all involve orchestrating legions of powerful computing machines to work together.) In the early 1980s, Lamport also created LaTeX, a document preparation system that provides sophisticated ways to typeset complex formulas and format scientific documents. In 1989, Lamport invented Paxos, a “consensus algorithm” that allows multiple computers to execute complex tasks; without it, modern computing could not exist. He’s also brought more attention to a handful of problems, giving them distinctive names like the bakery algorithm and the Byzantine Generals Problem. Lamport’s work since the 1990s has focused on “formal verification,” the use of mathematical proofs to verify the correctness of software and hardware systems. Notably, he created a “specification language” called TLA+ (for Temporal Logic of Actions), which employs the precise language of mathematics to prevent bugs and avoid design flaws.
Read more at Quanta Magazine: https://www.quantamagazine.org/bringing-mathematical-perfection-to-software-20220516/
- VISIT our Website: https://www.quantamagazine.org
- LIKE us on Facebook: https://www.facebook.com/QuantaNews
- FOLLOW us Twitter: https://twitter.com/QuantaMagazine
Quanta Magazine is an editorially independent publication supported by the Simons Foundation https://www.simonsfoundation.org/
#computerscience #math
- published: 17 May 2022
- views: 2896145
0:52
What is Feature Engineering? #100daysofai
Day 6 of #100DaysOfAI: Feature Engineering
Following up on yesterday’s discussion on data preparation, today we delve into feature engineering—transforming pr...
Day 6 of #100DaysOfAI: Feature Engineering
Following up on yesterday’s discussion on data preparation, today we delve into feature engineering—transforming prepared data into actionable business intelligence. By creating meaningful features and optimizing existing ones, you can significantly enhance the performance of your AI models. Here are some essential considerations for effective feature engineering in an enterprise context:
1. Creating New Features:
- Derived Features: Use existing data to create new features. For instance, calculate "customer tenure" by subtracting the signup date from the current date.
- Domain Knowledge: Leverage your industry knowledge to create features that capture essential aspects of your business. For example, features like "average purchase value" or "purchase frequency" should be created in retail.
2. Transforming Existpracticaling Features:
- Scaling: Normalize numerical features to ensure consistent data ranges, making it easier for models to process and interpret.
- Encoding Categorical Variables: Convert categorical data into a numerical format using techniques such as one-hot encoding or label encoding to make it usable for machine learning algorithms.
3. Handling Missing Values:
- Imputation Techniques: Building on our data preparation techniques, fill in missing data with statistical methods like mean, median, or mode, or use more advanced techniques like predictive imputation to maintain data integrity.
4. Managing Outliers:
- Outlier Detection: Identify outliers that could skew your model's predictions. Use domain knowledge to decide whether to transform, cap, or remove these outliers.
5. Data Transformation:
- Aggregation: Summarize data at different levels, such as customer or product level, to create summary statistics that capture trends and patterns.
Binning: Binning converts continuous variables into categorical variables. For example, "age" can be categorized into age groups like 18-25, 26-35, etc.
6. Feature Selection:
- Removing Redundant Features: Identify and remove features that do not add value to the model, such as those with low variance or high correlation with other features.
- Statistical Methods: Use techniques like correlation analysis and mutual information to select the most relevant features for your model.
Investing time in thorough feature engineering processes ensures your AI models are built on a solid foundation of high-quality data. This leads to more accurate models and reliable insights, ultimately driving the success of your AI projects in the enterprise. Stay tuned for more insights tomorrow!
https://wn.com/What_Is_Feature_Engineering_100Daysofai
Day 6 of #100DaysOfAI: Feature Engineering
Following up on yesterday’s discussion on data preparation, today we delve into feature engineering—transforming prepared data into actionable business intelligence. By creating meaningful features and optimizing existing ones, you can significantly enhance the performance of your AI models. Here are some essential considerations for effective feature engineering in an enterprise context:
1. Creating New Features:
- Derived Features: Use existing data to create new features. For instance, calculate "customer tenure" by subtracting the signup date from the current date.
- Domain Knowledge: Leverage your industry knowledge to create features that capture essential aspects of your business. For example, features like "average purchase value" or "purchase frequency" should be created in retail.
2. Transforming Existpracticaling Features:
- Scaling: Normalize numerical features to ensure consistent data ranges, making it easier for models to process and interpret.
- Encoding Categorical Variables: Convert categorical data into a numerical format using techniques such as one-hot encoding or label encoding to make it usable for machine learning algorithms.
3. Handling Missing Values:
- Imputation Techniques: Building on our data preparation techniques, fill in missing data with statistical methods like mean, median, or mode, or use more advanced techniques like predictive imputation to maintain data integrity.
4. Managing Outliers:
- Outlier Detection: Identify outliers that could skew your model's predictions. Use domain knowledge to decide whether to transform, cap, or remove these outliers.
5. Data Transformation:
- Aggregation: Summarize data at different levels, such as customer or product level, to create summary statistics that capture trends and patterns.
Binning: Binning converts continuous variables into categorical variables. For example, "age" can be categorized into age groups like 18-25, 26-35, etc.
6. Feature Selection:
- Removing Redundant Features: Identify and remove features that do not add value to the model, such as those with low variance or high correlation with other features.
- Statistical Methods: Use techniques like correlation analysis and mutual information to select the most relevant features for your model.
Investing time in thorough feature engineering processes ensures your AI models are built on a solid foundation of high-quality data. This leads to more accurate models and reliable insights, ultimately driving the success of your AI projects in the enterprise. Stay tuned for more insights tomorrow!
- published: 22 Jun 2024
- views: 495
25:22
Simplex Method Problem 1- Linear Programming Problems (LPP) - Engineering Mathematics - 4
Subject - Engineering Mathematics - 4
Video Name -Simplex Method Problem 1
Chapter - Linear Programming Problems (LPP)
Faculty - Prof. Farhan Meer
Upskill a...
Subject - Engineering Mathematics - 4
Video Name -Simplex Method Problem 1
Chapter - Linear Programming Problems (LPP)
Faculty - Prof. Farhan Meer
Upskill and get Placements with Ekeeda Career Tracks
Data Science - https://ekeeda.com/career-track/data-scientist
Software Development Engineer - https://ekeeda.com/career-track/software-development-engineer
Embedded & IoT Engineer - https://ekeeda.com/career-track/embedded-and-iot-engineer
Get FREE Trial for GATE 2023 Exam with Ekeeda GATE - 20000+ Lectures & Notes, strategy, updates, and notifications which will help you to crack your GATE exam.
https://ekeeda.com/catalog/competitive-exam
Coupon Code - EKGATE
Get Free Notes of All Engineering Subjects & Technology
https://ekeeda.com/digital-library
Access the Complete Playlist of Subject Engineering Mathematics - 4 -
https://www.youtube.com/playlist?list=PLm_MSClsnwm8ZKue0FAIDObAVKd3dfBSh
Social Links:
https://www.instagram.com/ekeeda_official/
https://in.linkedin.com/company/ekeeda.com
Happy Learning!
#SimplexMethodProblem1
#LinearProgrammingProblemsLPP
https://wn.com/Simplex_Method_Problem_1_Linear_Programming_Problems_(Lpp)_Engineering_Mathematics_4
Subject - Engineering Mathematics - 4
Video Name -Simplex Method Problem 1
Chapter - Linear Programming Problems (LPP)
Faculty - Prof. Farhan Meer
Upskill and get Placements with Ekeeda Career Tracks
Data Science - https://ekeeda.com/career-track/data-scientist
Software Development Engineer - https://ekeeda.com/career-track/software-development-engineer
Embedded & IoT Engineer - https://ekeeda.com/career-track/embedded-and-iot-engineer
Get FREE Trial for GATE 2023 Exam with Ekeeda GATE - 20000+ Lectures & Notes, strategy, updates, and notifications which will help you to crack your GATE exam.
https://ekeeda.com/catalog/competitive-exam
Coupon Code - EKGATE
Get Free Notes of All Engineering Subjects & Technology
https://ekeeda.com/digital-library
Access the Complete Playlist of Subject Engineering Mathematics - 4 -
https://www.youtube.com/playlist?list=PLm_MSClsnwm8ZKue0FAIDObAVKd3dfBSh
Social Links:
https://www.instagram.com/ekeeda_official/
https://in.linkedin.com/company/ekeeda.com
Happy Learning!
#SimplexMethodProblem1
#LinearProgrammingProblemsLPP
- published: 08 Feb 2021
- views: 594420