Radial basis function network

RBF Networks

Radial Basis Function Networks are not talked about a lot these days, but they are very interesting and useful. Handwriting demo: http://macheads101.com/demos/handwriting/?c=rbf Resizing images with RBF networks: https://github.com/unixpickle/rbfscale#results Distance formula in kNN vid

From playlist Machine Learning

Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

Support Vector Machines use kernel functions to do all the hard work and this StatQuest dives deep into one of the most popular: The Radial (RBF) Kernel. We talk about the parameter values, how they calculate high-dimensional coordinates and then we'll figure out, step-by-step, how the Rad

From playlist Support Vector Machines

1 Vectors

A short refresher on vectors. Before I introduce vector-based functions, it's important to look at vectors themselves and how they are represented in python™ and the IPython Notebook using SymPy.

From playlist Life Science Math: Vectors

Gridlines in nonstandard coordinate systems.

Description: We use gridlines to conveniently plot points in the standard coordinate system all the time. But in a coordinate system defined by some other basis, there is a natural analogue of coordinate systems. Learning Objectives: 1) Given a basis, sketch a few gridlines 2) Find a poi

From playlist Older Linear Algebra Videos

Physics Ch 67.1 Advanced E&M: Review Vectors (88 of 113) Curl in Spherical Coordinates Ex. 1

Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will calculate the curl in spherical coordinates of v vector, given v=r(r-hat). Example 1 Next video in this series can be seen a

From playlist PHYSICS 67.1 ADVANCED E&M VECTORS & FIELDS

Multilayer Neural Networks - Part 1: Introduction

This video is about Multilayer Neural Networks - Part 1: Introduction Abstract: This is a series of video about multi-layer neural networks, which will walk through the introduction, the architecture of feedforward fully-connected neural network and its working principle, the working prin

From playlist Neural Networks

Dual basis

Dual basis definition and proof that it's a basis In this video, given a basis beta of a vector space V, I define the dual basis beta* of V*, and show that it's indeed a basis. We'll see many more applications of this concept later on, but this video already shows that it's straightforwar

From playlist Dual Spaces

Lecture 16 - Radial Basis Functions

Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 16 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/cours

From playlist Machine Learning Course - CS 156

Tess Smidt: "Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomi..."

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Euclidean Neural Networks* for Emulating Ab Initio Calculations and Generating Atomic Geometries *also called Tensor Field Networks and 3D

From playlist Machine Learning for Physics and the Physics of Learning 2019

Linear Algebra 4.7 Change of Basis

My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul

From playlist Linear Algebra

Lecture 20 | MIT 6.832 Underactuated Robotics, Spring 2009

Lecture 20: Temporal difference learning with function approximation 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

Mihail Bogojeski - Message passing neural networks for atomistic systems: Molecules - IPAM at UCLA

Recorded 01 April 2022. Mihail Bogojeski of Technische Universität Berlin presents "Message passing neural networks for atomistic systems: Molecules" at IPAM's Multiscale Approaches in Quantum Mechanics Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-i-

From playlist 2022 Multiscale Approaches in Quantum Mechanics Workshop

Deep Learning Tutorial for Beginners | Deep Learning 2022 | Deep Learning Explained | Simplilearn

🔥Free Deep Learning Course With Completion Certificate: https://www.simplilearn.com/introduction-to-deep-learning-free-course-skillup?utm_campaign=DeepLearningTutorialforBeginners&utm_medium=Description&utm_source=youtube ✅Subscribe to our Channel to learn more about the top Technologies

From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn

Radial Basis Function Kernel : Data Science Concepts

The *most powerful* kernel in all the land. SVM Kernels Video: https://youtu.be/OKFMZQyDROI My Patreon : https://www.patreon.com/user?u=49277905

From playlist Data Science Concepts

Exercise 11: Eligibility Traces

Eleventh tutorial video of the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials

From playlist Reinforcement Learning Course: Tutorials (Summer 2020)

Tensor Field Networks | AISC

Toronto Deep Learning Series, 11-Feb-2019 https://tdls.a-i.science/events/2019-02-11 TENSOR FIELD NETWORKS: ROTATION- AND TRANSLATION-EQUIVARIANT NEURAL NETWORKS FOR 3D POINT CLOUDS We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, a

From playlist Math and Foundations

Koopman Spectral Analysis (Representations)

In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression. This includes the use of sparse regression and neural networks, and highlights the importance of cross-validating. https://www.eigensteve.com/

From playlist Koopman Analysis

Math 060 Fall 2017 111317C Orthonormal Bases

Motivation: how to obtain the coordinate vector with respect to a given basis? Definition: orthogonal set. Example. Orthogonal implies linearly independent. Orthonormal sets. Example of an orthonormal set. Definition: orthonormal basis. Properties of orthonormal bases. Example: Fou

From playlist Course 4: Linear Algebra (Fall 2017)