This video explains what information the gradient provides about a given function. http://mathispower4u.wordpress.com/
From playlist Functions of Several Variables - Calculus
Download the free PDF http://tinyurl.com/EngMathYT A basic tutorial on the gradient field of a function. We show how to compute the gradient; its geometric significance; and how it is used when computing the directional derivative. The gradient is a basic property of vector calculus. NOT
From playlist Engineering Mathematics
What is Gradient, and Gradient Given Two Points
"Find the gradient of a line given two points."
From playlist Algebra: Straight Line Graphs
Gradient (1 of 3: Developing the formula)
More resources available at www.misterwootube.com
From playlist Further Linear Relationships
Gradient Descent : Data Science Concepts
A technique that comes up over and over again in all parts of data science! Link to Code : https://github.com/ritvikmath/YouTubeVideoCode/blob/main/Gradient%20Descent.ipynb My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Code
Introduction to Gradient (2 of 2: Reading & Interpreting Graphs)
More resources available at www.misterwootube.com
From playlist Linear Relationships
Physics - Advanced E&M: Ch 1 Math Concepts (9 of 55) What is the Gradient of a Scalar?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain the gradient of a scalar. Next video in this series can be seen at: https://youtu.be/QeQukYCLCpE
From playlist PHYSICS 67 ADVANCED ELECTRICITY & MAGNETISM
Mathematics for Machine Learning: What is Gradient?
In this video, we talk about gradients in machine learning, data science, and optimization. The generalization of the derivative to functions of several variables is the gradient. This tutorial is designed to be easy to understand with basic knowledge of Calculus 1. Finding gradients is im
From playlist Mathematics for Machine Learning - Dr. Data Science Series
See also https://youtu.be/W2pSn_t0KYs and https://youtu.be/x7QYZ4n3A8M
From playlist gradient_descent
A Geometric View on Private Gradient-Based Optimization
A Google TechTalk, presented by Steven Wu, 2021/04/16 ABSTRACT: Differential Privacy for ML Series. Deep learning models are increasingly popular in many machine learning applications where the training data may contain sensitive information. To provide formal and rigorous privacy guaran
From playlist Differential Privacy for ML
Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTry This lecture covers supervised learning and linear regression. Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow alon
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3c7n6jW Professor Christopher Manning & PhD Candidate Abigail See, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Sieb
From playlist Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019
Chiara Cammarota: "High-dimensional cost landscape and gradient descent in Tensor PCA and its ge..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "High-dimensional cost landscape and gradient descent in Tensor PCA and its generalisations" Chiara Cammarota - King's College London Abstract: Tensor PCA is a prototy
From playlist Machine Learning for Physics and the Physics of Learning 2019
Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3nAk9O3 Topics: Linear classification, Loss minimization, Stochastic gradient descent Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanfor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 9 - Policy Gradient II
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 8 - Policy Gradient I
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
Lecture 4 | Convex Optimization II (Stanford)
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd lectures on subgradient methods for constrained problems. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Dec
From playlist Lecture Collection | Convex Optimization
Federated Learning with Formal User-Level Differential Privacy Guarantees
A Google TechTalk, presented by Abhradeep Thakurta, 2022/11/10. Presented at the 2022 Workshop on Federated Learning and Analytics. About the speaker: Bio: Abhradeep Guha Thakurta is a staff research scientist at Google Research on the Brain Team. His research lies in the intersection of
From playlist 2022 Workshop on Federated Learning and Analytics
The gradient captures all the partial derivative information of a scalar-valued multivariable function.
From playlist Multivariable calculus