More videos like this online at http://www.theurbanpenguin.com Understanding variable scope in Java. We take a quick look at Class, Instance and Local variables and see how scope affects their access.
From playlist Java
Intro to a Variable as a Changing Value or Placeholder
This video defines a variable and provides examples of a variable used as a changing value or a placeholder http://mathispower4u.com
From playlist Algebraic Structures Module
Statistics: Ch 5 Discrete Random Variable (2 of 27) What is a Discrete Random Variable?
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 learn a discrete random variable can be a count of something, an integer, as how many times a coin comes up “heads” or “tails
From playlist STATISTICS CH 5 DISCRETE RANDOM VARIABLE
Classify a polynomial then determining if it is a polynomial or not
👉 Learn how to determine whether a given equation is a polynomial or not. A polynomial function or equation is the sum of one or more terms where each term is either a number, or a number times the independent variable raised to a positive integer exponent. A polynomial equation of functio
From playlist Is it a polynomial or not?
Fixed Effects and Random Effects
Brief overview in plain English of the differences between the types of effects. Problems with each model and how to overcome them.
From playlist Experimental Design
Is it a polynomial with two variables
👉 Learn how to determine whether a given equation is a polynomial or not. A polynomial function or equation is the sum of one or more terms where each term is either a number, or a number times the independent variable raised to a positive integer exponent. A polynomial equation of functio
From playlist Is it a polynomial or not?
Determining if a equation is a polynomial or not
👉 Learn how to determine whether a given equation is a polynomial or not. A polynomial function or equation is the sum of one or more terms where each term is either a number, or a number times the independent variable raised to a positive integer exponent. A polynomial equation of functio
From playlist Is it a polynomial or not?
Introduction to Discrete and Continuous Variables
This video defines and provides examples of discrete and continuous variables.
From playlist Introduction to Functions: Function Basics
Hidden Variables—How We Know They Don't Exist In Quantum Mechanics
In this video I show you what a hidden variable is and then show you a proof of Bell's Theorem that shows how we know that hidden variables don't exist in quantum mechanics. Some of the information in this video was adapted from DrPhysicsA (https://www.youtube.com/watch?v=qd-tKr0LJTM). I h
From playlist The Action Lab Does Quantum Mechanics
Restricted Boltzmann Machines: Stastical Physics and applications... by Simona Cocco
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
Discovering Variables – Combining Numbers for More Powerful Statistics (1-4)
Combining numbers creates variables – values that can vary or take on more than one value. If a value can be measured among a group and that value will be different for at least some of the group members, then you are measuring a variable. You will learn about qualitative (categorical) and
From playlist WK1 Numbers and Variables - Online Statistics for the Flipped Classroom
Lecture 13/16 : Stacking RBMs to make Deep Belief Nets
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 13A The ups and downs of backpropagation 13B Belief Nets 13C Learning Sigmoid Belief Nets 13D The wake-sleep algorithm
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Investigate the forward backward algorithm of hidden Markov models, by deriving the backward algorithm using reverse mode automatic differentiation.
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
Hidden Markov Model : Data Science Concepts
All about the Hidden Markov Model in data science / machine learning
From playlist Data Science Concepts
Data Science - Part VIII - Artifical Neural Network
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of biological based learning in the brain and how to simulate this approach thr
From playlist Data Science
Graham Taylor: "Learning Representations of Sequences"
Graduate Summer School 2012: Deep Learning, Feature Learning "Learning Representations of Sequences" Graham Taylor, University of Guelph Institute for Pure and Applied Mathematics, UCLA July 13, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer
From playlist GSS2012: Deep Learning, Feature Learning
Lecture 13C : Learning Sigmoid Belief Nets
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 13C : Learning Sigmoid Belief Nets
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Conceptual Questions about Random Variables and Probability Distributions
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Conceptual Questions about Random Variables and Probability Distributions
From playlist Statistics