Mathematical modeling | Computational fluid dynamics
Gradient-enhanced kriging (GEK) is a surrogate modeling technique used in engineering. A surrogate model (alternatively known as a metamodel, response surface or emulator) is a prediction of the output of an expensive computer code. This prediction is based on a small number of evaluations of the expensive computer code. (Wikipedia).
Gradient Descent, Step-by-Step
Gradient Descent is the workhorse behind most of Machine Learning. When you fit a machine learning method to a training dataset, you're probably using Gradient Descent. It can optimize parameters in a wide variety of settings. Since it's so fundamental to Machine Learning, I decided to mak
From playlist Optimizers in Machine Learning
Gradient Boosting : Data Science's Silver Bullet
A dive into the all-powerful gradient boosting method! My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
This video explains what information the gradient provides about a given function. http://mathispower4u.wordpress.com/
From playlist Functions of Several Variables - Calculus
This video follows on from the discussion on linear regression as a shallow learner ( https://www.youtube.com/watch?v=cnnCrijAVlc ) and the video on derivatives in deep learning ( https://www.youtube.com/watch?v=wiiPVB9tkBY ). This is a deeper dive into gradient descent and the use of th
From playlist Introduction to deep learning for everyone
Stefano Marelli: Metamodels for uncertainty quantification and reliability analysis
Abstract: Uncertainty quantification (UQ) in the context of engineering applications aims aims at quantifying the effects of uncertainty in the input parameters of complex models on their output responses. Due to the increased availability of computational power and advanced modelling tech
From playlist Probability and Statistics
Geostatistics session 3 universal kriging
Introduction to Universal Kriging
From playlist Geostatistics GS240
11_3_1 The Gradient of a Multivariable Function
Using the partial derivatives of a multivariable function to construct its gradient vector.
From playlist Advanced Calculus / Multivariable Calculus
12d Python Data Analytics: Simple Kriging
An interactive demonstration of simple kriging in Python. To follow along the Python Jupyter Notebook is available here: https://git.io/JfIpD.
From playlist Data Analytics and Geostatistics
Lecture on the motivation for simulation vs. estimation and development of the sequential Gaussian simulation approach.
From playlist Data Analytics and Geostatistics
Deep Learning Lecture 4.3 - Stochastic Gradient Descent
Deep Learning Lecture: Optimization Methods - Stochastic Gradient Descent (SGD) - SGD with Momentum
From playlist Deep Learning Lecture
Ensembles (3): Gradient Boosting
Gradient boosting ensemble technique for regression
From playlist cs273a
12b Geostatistics Course: Kriging
Lecture on kriging for spatial estimation.
From playlist Data Analytics and Geostatistics
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
14 Data Analytics: Indicator Methods
Lecture on the use of indicators for spatial estimation and simulation.
From playlist Data Analytics and Geostatistics
Gradient Boost Part 2 (of 4): Regression Details
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 second part in a series that walks through it one step at a time. This video focuses on the original Gradient Boost algorithm used to predict a continuou
From playlist StatQuest
Geostatistics session 5 conditional simulation
Introduction to conditional simulation with Gaussian processes
From playlist Geostatistics GS240