Greedy Algorithm | What Is Greedy Algorithm? | Introduction To Greedy Algorithms | Simplilearn
This video on the Greedy Algorithm will acquaint you with all the fundamentals of greedy programming paradigm. In this tutorial, you will learn 'What Is Greedy Algorithm?' with the help of suitable examples. And finally, you will also discover few important applications of greedy algorithm
From playlist Data Structures & Algorithms [2022 Updated]
Linear Algebra 9a: Introduction to Gaussian Elimination
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 1 Linear Algebra: An In-Depth Introduction with a Focus on Applications
Empirical Mode Decomposition (1D, univariate approach)
Introduction to the Empirical Mode Decomposition - EMD - (one-dimensional, univariate version), which is a data decomposition method for non-linear and non-stationary data. This video covers the main features of the EMD and the working principle of the algorithm. The EMD is briefly compar
From playlist Summer of Math Exposition Youtube Videos
Newton-Raphson Algorithm in Python | Finding real and complex roots systematically
The algorithm explained: https://www.youtube.com/watch?v=qlNqPE_X4ME In this video tutorial I show you how to implement the Newton-Raphson algorithm in Python. The basic algorithm is very straightforward to implement, but there are a few tricks and things to keep in mind. In the video I s
From playlist Newton-Raphson Algorithm
Build a Heap - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Jensen Huang — NVIDIA's CEO on the Next Generation of AI and MLOps
Jensen Huang is founder and CEO of NVIDIA, whose GPUs sit at the heart of the majority of machine learning models today. Jensen shares the story behind NVIDIA's expansion from gaming to deep learning acceleration, leadership lessons that he's learned over the last few decades, and why we
From playlist Top 10: MLOps Tutorials and Talks
[Calculus] Newton's Method || Lecture 36
Visit my website: http://bit.ly/1zBPlvm Subscribe on YouTube: http://bit.ly/1vWiRxW Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any que
From playlist Calculus 1
Russian Multiplication Algorithm Solution - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Peeking into the black box - Grace Huang (Pinterest)
Peeking into the black box: Lessons from the front lines of machine-learning product launches With advances in machine-learning algorithms and the democratization of big data technologies, machine-learning products have become ubiquitous—they are the de facto choice for powering the exper
From playlist Strata Data Conference 2017 - London, United Kingdom
Discouraged with Data Science? - Watch THIS video.
Feeling down on your data science journey? This video may just help. I go through how I have dealt with feelings of overwhelm, inadequacy, hopelessness, confusion, stress, and anxiety during my data science learning. 365 Data Science - Courses ( 57% Annual Discount): https://365datascience
From playlist Learning, Productivity, and Motivation
Sorrachai Yingchareonthawornchai: Approximating k-Edge-Connected Spanning Subgraphs via a Fast [...]
Full title: Approximating k-Edge-Connected Spanning Subgraphs via a Fast Linear Program Solver In the k-edge-connected spanning subgraph (kECSS) problem, our goal is to compute a minimum-cost sub-network that is resilient against up to k link failures: Given an n-node m-edge graph with cos
From playlist Workshop: Continuous approaches to discrete optimization
When Risk Taking Goes Too Far - The Archegos Collapse
Click the link to sign up to Wise, the world’s most international account: http://bit.ly/coldfusionandwise ColdFusion Discord: https://discord.gg/3WWKmzqMPY --- About ColdFusion --- ColdFusion is an Australian based online media company independently run by Dagogo Altraide since 2009. T
From playlist All My Videos
ch5 6: Numerical Solutions of nonlinear equations. Newton's iteration. Wen Shen
Wen Shen, Penn State University. Lectures are based on my book: "An Introduction to Numerical Computation", published by World Scientific, 2016. See promo video: https://youtu.be/MgS33HcgA_I
From playlist CMPSC/MATH 451 Videos. Wen Shen, Penn State University
Centrality - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
AI beats us at another game: STRATEGO | DeepNash paper explained
DeepMind made an expert-level Stratego bot. We explain how they program an unexploitable AI player and we go into more details while explaining their model-free Reinforcement Learning method and how they achieve Nash Equilibrium with Regularized Nash Dynamics. ► Sponsor: NVIDIA: 👉 https:/
From playlist Explained AI/ML in your Coffee Break
Han Huang - When can we recover an Erdos-Renyi graph from its local structure? - IPAM at UCLA
Recorded 09 February 2022. Han Huang of the Georgia Institute of Technology Department of Mathematics presents "When can we recover an Erdos-Renyi graph from its local structure?" at IPAM's Calculus of Variations in Probability and Geometry Workshop. Abstract: Suppose we have a graph G. F
From playlist Workshop: Calculus of Variations in Probability and Geometry
Solving an exponential equation using the one to one property 16^x + 2 = 6
👉 Learn how to solve exponential equations. An exponential equation is an equation in which a variable occurs as an exponent. To solve an exponential equation, we isolate the exponential part of the equation. Then we take the log of both sides. Note that the base of the log should correspo
From playlist Solve Exponential Equations with Logarithms
Brice Huang (MIT) -- The Algorithmic Phase Transition of Random k-SAT for Low Degree Polynomials
Let $\Phi$ be a uniformly random $k$-SAT formula with $n$ variables and $m$ clauses. We study the algorithmic task of finding a satisfying assignment of $\Phi$. It is known that satisfying assignments exist with high probability up to clause density $m/n = 2^k \log 2 - \frac12 (\log 2 + 1)
From playlist Northeastern Probability Seminar 2021
Naive Multiplication Algorithm Solution - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms