In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation. It is a form of discretization in general and also of binning, as in making a histogram. Whenever continuous data is discretized, there is always some amount of discretization error. The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand. Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies). Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others Many machine learning algorithms are known to produce better models by discretizing continuous attributes. (Wikipedia).
Properties of Fourier Transforms
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Basic properties of the Fourier transform and discrete-time Fourier transform: convolution-multiplication, multiplication-convolution (windowi
From playlist Introduction and Background
11_3_6 Continuity and Differentiablility
Prerequisites for continuity. What criteria need to be fulfilled to call a multivariable function continuous.
From playlist Advanced Calculus / Multivariable Calculus
Learn to find the value that makes the piecewise function differentiable and continuous
👉 Learn how to determine the differentiability of a function. A function is said to be differentiable if the derivative exists at each point in its domain. To check the differentiability of a function, we first check that the function is continuous at every point in the domain. A function
From playlist Find the Differentiability of a Function
How to determine if a function is continuous and differentiable
👉 Learn how to determine the differentiability of a function. A function is said to be differentiable if the derivative exists at each point in its domain. To check the differentiability of a function, we first check that the function is continuous at every point in the domain. A function
From playlist Find the Differentiability of a Function
How to find REMOVABLE DISCONTINUITIES (KristaKingMath)
► My Limits & Continuity course: https://www.kristakingmath.com/limits-and-continuity-course Discontinuities can be characterized as either removable or nonremovable. Removable discontinuities are also called point discontinuities, because they are small holes in the graph of a function a
From playlist Calculus I
Review of Linear Time Invariant Systems
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Review: systems, linear systems, time invariant systems, impulse response and convolution, linear constant-coefficient difference equations
From playlist Introduction and Background
Continuity Basic Introduction, Point, Infinite, & Jump Discontinuity, Removable & Nonremovable
This calculus video tutorial provides a basic introduction into to continuity. It explains the difference between a continuous function and a discontinuous one. It discusses the difference between a jump discontinuity, an infinite discontinuity and a point discontinuity. A point discont
From playlist New Calculus Video Playlist
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
Lecture 17 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a
From playlist Lecture Collection | Machine Learning
Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3njDdzN Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
A geometric integration approach to non-smooth (...) - Schoenlieb/Riis - Workshop 1 - CEB T1 2019
Schoenlieb/Riis (University of Cambridge) / 04.02.2019 A geometric integration approach to non-smooth and non-convex optimisation The optimisation of nonsmooth, nonconvex functions without access to gradients is a particularly challenging problem that is frequently encountered, for exam
From playlist 2019 - T1 - The Mathematics of Imaging
Determine Where the Function is Not Continuous
In this video I will show you how to Determine Where the Function is Not Continuous.
From playlist Continuity Problems
Neža Mramor (2/17/21): An application of discrete Morse theory to robot motion planning
Title: An application of discrete Morse theory to robot motion planning Abstract: We will shortly recollect the basics of discrete Morse theory and two of its variants, parametric and fiberwise discrete Morse theory. We will then describe how it can be used to construct a continuous motio
From playlist AATRN 2021
Metamorphosis on generalized image manifolds - Rumpf - Workshop 1 - CEB T1 2019
Rumpf (Univ Bonn) / 07.02.2019 Metamorphosis on generalized image manifolds In the metamorphosis model the space of images is equipped with a Riemannian metric measuring both the cost of transport of image intensities and the variation of them along motion lines. In this talk a recently
From playlist 2019 - T1 - The Mathematics of Imaging
Mathematical and Computational Aspects of Machine Learning - 11 October 2019
http://www.crm.sns.it/event/451/timetable.html#title 10:00- 11:00 Ruthotto, Lars Numerical Methods for Deep Learning 11:00- 11:30 Coffee break 11:30- 12:30 Grohs, Philipp Approximation theory, Numerical Analysis and Deep Learning 14:30- 15:30 Grohs, Philipp Approximation theory, Numer
From playlist Centro di Ricerca Matematica Ennio De Giorgi
Lars Ruthotto: "Deep Neural Networks Motivated By Differential Equations (Part 1/2)"
Watch part 2/2 here: https://youtu.be/1mVycBKb1TE Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Deep Neural Networks Motivated By Differential Equations (Part 1/2)" Lars Ruthotto, Emory University Abstract: In this short course, we establish the connection bet
From playlist Machine Learning for Physics and the Physics of Learning 2019
Robot Dog Learns to Walk - Bittle Reinforcement Learning p.3
Further progress with using reinforcement learning to train robot dogs/quadrupeds to walk Neural Networks from Scratch book: https://nnfs.io autoencoders tutorial: https://pythonprogramming.net/autoencoders-tutorial/ The actual Petoi Bittle robot can be found here: https://www.petoi.com/
From playlist Physics Simulator w/ Robot Dog
Stanford CS229: Machine Learning | Summer 2019 | Lecture 15 - Reinforcement Learning - II
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3E8Do7X Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Introduction to Discrete and Continuous Functions
This video defines and provides examples of discrete and continuous functions.
From playlist Introduction to Functions: Function Basics