generative model vs discriminative model
understanding difference between generative model and discriminative model with simple example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
What are the Types of Numbers? Real vs. Imaginary, Rational vs. Irrational
We've mentioned in passing some different ways to classify numbers, like rational, irrational, real, imaginary, integers, fractions, and more. If this is confusing, then take a look at this handy-dandy guide to the taxonomy of numbers! It turns out we can use a hierarchical scheme just lik
From playlist Algebra 1 & 2
Introduction to Classification Models
Ever wonder what classification models do? In this quick introduction, we talk about what classifications models are, as well as what they are used for in machine learning. In machine learning there are many different types of models, all with different types of outcomes. When it comes t
From playlist Introduction to Machine Learning
Category Theory: The Beginner’s Introduction (Lesson 1 Video 4)
Lesson 1 is concerned with defining the category of Abstract Sets and Arbitrary Mappings. We also define our first Limit and Co-Limit: The Terminal Object, and the Initial Object. Other topics discussed include Duality and the Opposite (or Mirror) Category. These videos will be discussed
From playlist Category Theory: The Beginner’s Introduction
(ML 1.5) Generative vs discriminative models
A broad overview. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Object Oriented Programming 1 - Classes and Objects
This is the first in a series of videos that introduce object oriented programming (OOP) using Visual Basic.NET (VB.NET). This video explains the relationship between a class and an object. It shows how the public interface of a custom class can be coded, by declaring public variables wi
From playlist Object Oriented Programming
HTML5 CSS3 tutorial - Defining and applying a CSS class
This tutorial explains the concept of classes in CSS and how to apply it to different tags.
From playlist HTML5 and CSS3
Multilevel Latent Class Regression of Stages of Change for Multiple Health Behaviors
Multilevel Laten Class Regression of Stages of Change for Multiple Health Behaviors, recorded November 26th, 2012. For more information and access to courses, lectures, and teaching material, please visit the official UC Irvine OpenCourseWare website at: http://ocw.uci.edu
From playlist Public Health: Collections
Introduction to Classification | Predictive Modeling and Machine Learning, Part 2
This video covers the basics of the most common machine learning classification models that you can tune to work with any number of predictor variables. Each has its advantages and disadvantages in terms of accuracy and training speed. The only way to know which one works best on a particu
From playlist Predictive Modeling and Machine Learning
From playlist Plenary talks One World Symposium 2020
Latent class cluster analysis with free software Jamovi
In this video, I will show how to do a latent class cluster analysis with free software Jamovi. Please download Jamovi from this link: https://www.jamovi.org/download.html Recommended papers: 1. Latent class cluster analysis paper: https://journals.sagepub.com/doi/abs/10.1177/0276236619
From playlist Jamovi software
CMU Neural Nets for NLP 2017 (15): Latent Variable Models
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Generative vs. Discriminative, Deterministic vs. Random Variables * Variational Autoencoders * Handling Discrete Latent Variables * Examples of Variational Autoencoders in NLP Slides: http://
From playlist CMU Neural Nets for NLP 2017
TMCF workshop - Enriching latent class models with counterfactual prediction, Mark Gilthorpe
Prediction algorithms in AI use machine learning and statistics to make predictions about an event, given what we know now. Examples include whether a covid-19 patient will require ventilation, or whether a person seeking insurance will make a claim. These predictions can be used for plann
From playlist Theory and Methods Challenge Fortnights
Ellen Zhoung - Machine learning for determining protein structure and dynamics from cryo-EM images
Recorded 14 November 2022. Ellen Zhong of Princeton University presents "Machine learning for determining protein structure and dynamics from cryo-EM images" at IPAM's Cryo-Electron Microscopy and Beyond Workshop. Abstract: Major technological advances in cryo-electron microscopy (cryo-EM)
From playlist 2022 Cryo-Electron Microscopy and Beyond
Revisiting non-linear PCA with progressively grown autoencoders. - Lezama - Workshop 3 - CEB T1 2019
José Lezama (Universidad de la Republica) / 02.04.2019 Revisiting non-linear PCA with progressively grown autoencoders. In this talk I will revisit the old problem of nonlinear dimensionality reduction with hierarchical representations. That is, representations where the first n compone
From playlist 2019 - T1 - The Mathematics of Imaging
DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks
Generative adversarial networks (GANs), first proposed by Ian Goodfellow et al. in 2014, have emerged as one of the most promising approaches to generative modeling, particularly for image synthesis. In their most basic form, they consist of two "competing" networks: a generator which trie
From playlist Learning resources
CS231n Lecture 14 - Videos and Unsupervised Learning
ConvNets for videos Unsupervised learning
From playlist CS231N - Convolutional Neural Networks
Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Formal Definition of a Function using the Cartesian Product
Learning Objectives: In this video we give a formal definition of a function, one of the most foundation concepts in mathematics. We build this definition out of set theory. **************************************************** YOUR TURN! Learning math requires more than just watching vid
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)