Gradient Boost Part 1 (of 4): Regression Main Ideas
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous
From playlist StatQuest
Lesson: Calculate a Confidence Interval for a Population Proportion
This lesson explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals
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
Gradient (1 of 3: Developing the formula)
More resources available at www.misterwootube.com
From playlist Further Linear Relationships
Rohitash Chandra - BERT-based language models for US Elections, COVID-19, and analysis
Dr Rohitash Chandra (UNSW Sydney) presents "BERT-based language models for US Elections, COVID-19, and analysis of the translations of the Bhagavad Gita", 26 November 2021.
From playlist Statistics Across Campuses
Cecilia Clementi: "Learning molecular models from simulation and experimental data"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Learning molecular models from simulation and experimental data" Cecilia Clementi - Rice University Institute for Pure and Applied Mathematics, UCLA October 14, 2019 F
From playlist Machine Learning for Physics and the Physics of Learning 2019
Example on gradient identities for functions of two variables.
From playlist Engineering Mathematics
Bao Wang: "Momentum in Stochastic Gradient Descent and Deep Neural Nets"
Deep Learning and Medical Applications 2020 "Momentum in Stochastic Gradient Descent and Deep Neural Nets" Bao Wang - University of California, Los Angeles (UCLA), Mathematics Abstract: Stochastic gradient-based optimization algorithms play perhaps the most important role in modern machi
From playlist Deep Learning and Medical Applications 2020
Rafael Gómez-Bombarelli: "Coarse graining autoencoders and evolutionary learning of atomistic..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Coarse graining autoencoders and evolutionary learning of atomistic potentials" Rafael Gomez-Bombarelli, Massachusetts Institute of Technol
From playlist Machine Learning for Physics and the Physics of Learning 2019
Finding The Gradient Of A Straight Line | Graphs | Maths | FuseSchool
The gradient of a line tells us how steep the line is. Lines going in this / direction have positive gradients, and lines going in this \ direction have negative gradients. The gradient can be found by finding how much the line goes up - the rise, and dividing it by how much the line goe
From playlist MATHS
Lightning Talks - Chi Jin, Lin Yang, Alec Koppel, Karan Singh, Nataly Brukhim
Workshop on New Directions in Reinforcement Learning and Control Topic:Lightning Talks Speaker: Chi Jin, Lin Yang, Alec Koppel, Karan Singh, Nataly Brukhim Date: November 8, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
DeepMind x UCL RL Lecture Series - Exploration & Control [2/13]
Research Scientist Hado van Hasselt looks at why it's important for learning agents to balance exploring and exploiting acquired knowledge at the same time. Slides: https://dpmd.ai/explorationcontrol Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause
A Google TechTalk, presented by Andreas Krause, 2021/06/07 ABSTRACT: A central challenge in Bayesian Optimization and related tasks is the exploration—exploitation dilemma: Selecting inputs that are informative about the unknown function, while focusing exploration where we expect high ret
From playlist Google BayesOpt Speaker Series 2021-2022
Emilie Chouzenoux - Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods have started to be widely applied to ill-posed inverse problems since they have the ability to embed prior knowledge about the solution. However, the level of performance of these methods significantly depends on a set of parameters, which can be estimated through compu
From playlist Journée statistique & informatique pour la science des données à Paris-Saclay 2021
What is Gradient, and Gradient Given Two Points
"Find the gradient of a line given two points."
From playlist Algebra: Straight Line Graphs