Introduction to Gradient (2 of 2: Reading & Interpreting Graphs)
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From playlist Linear Relationships
Introduction to the Gradient Theory and Formulas
Introduction to the Gradient Theory and Formulas 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 Thank you:)
From playlist Calculus 3
Example on gradient identities for functions of two variables.
From playlist Engineering Mathematics
The gradient captures all the partial derivative information of a scalar-valued multivariable function.
From playlist Multivariable calculus
This video explains what information the gradient provides about a given function. http://mathispower4u.wordpress.com/
From playlist Functions of Several Variables - Calculus
Gradient (1 of 3: Developing the formula)
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From playlist Further Linear Relationships
See also https://youtu.be/W2pSn_t0KYs and https://youtu.be/x7QYZ4n3A8M
From playlist gradient_descent
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
What Does the Gradient Vector Mean Intuitively?
What Does the Gradient Vector Mean Intuitively? 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 Thank you:)
From playlist Calculus 3
Gradients are Not All You Need (Machine Learning Research Paper Explained)
#deeplearning #backpropagation #simulation More and more systems are made differentiable, which means that accurate gradients of these systems' dynamics can be computed exactly. While this development has led to a lot of advances, there are also distinct situations where backpropagation c
From playlist Papers Explained
Lecture 8 | MIT 6.832 Underactuated Robotics, Spring 2009
Lecture 8: Dynamic programming (DP) and policy search Instructor: Russell Tedrake See the complete course at: http://ocw.mit.edu/6-832s09 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.832 Underactuated Robotics, Spring 2009
Lecture 12 | MIT 6.832 Underactuated Robotics, Spring 2009
Lecture 12: Walking (continued) Instructor: Russell Tedrake See the complete course at: http://ocw.mit.edu/6-832s09 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.832 Underactuated Robotics, Spring 2009
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 6 - Reinforcement Learning Primer
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ 0:00 Introduction 0:46 Logistics 2:31 Why Reinforcement Learning? 3:37 The Pla
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
Exploring the random landscapes of inference (Lecture 3) by Gérard Ben Arous
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
Reinforcement Learning Series: Overview of Methods
This video introduces the variety of methods for model-based and model-free reinforcement learning, including: dynamic programming, value and policy iteration, Q-learning, deep RL, TD-learning, SARSA, policy gradient optimization, among others. This is the overview in a series on reinfo
From playlist Reinforcement Learning
Anter El-Azab: Mesoscale crystal plasticity based on continuum dislocation dynamics
Anter El-Azab: Mesoscale crystal plasticity based on continuum dislocation dynamics: mathematical formalism and numerical solution The lecture was held within the framework of the Hausdorff Trimester Program Multiscale Problems: Workshop on Non-local Material Models and Concurrent Multisc
From playlist HIM Lectures: Trimester Program "Multiscale Problems"
Hydrodynamics, variational principles and integrability (Pedagogical Lecture 3) by Alexander Abanov
PROGRAM: INTEGRABLE SYSTEMS IN MATHEMATICS, CONDENSED MATTER AND STATISTICAL PHYSICS ORGANIZERS: Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE : 16 July 2018 to 10 August 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore
From playlist Integrable systems in Mathematics, Condensed Matter and Statistical Physics
11_7_1 Potential Function of a Vector Field Part 1
The gradient of a function is a vector. n-Dimensional space can be filled up with countless vectors as values as inserted into a gradient function. This is then referred to as a vector field. Some vector fields have potential functions. In this video we start to look at how to calculat
From playlist Advanced Calculus / Multivariable Calculus
Non-equilibrium dynamics of inhomogeneous fluids by Sutapa Roy
Inhomogeneous fluids are interesting due to the inherent presence of interfaces and they give rise to a plethora of phenomena that cannot be observed in bulk. Typical examples include confined liquids where the presence of surfaces not only changes their static properties compared to in th
From playlist ICTS Colloquia