Intro to Graphs and Label Propagation Algorithm in Machine Learning
Join my FREE course Basics of Graph Neural Networks (https://www.graphneuralnets.com/p/basics-of-gnns/?src=yt)! Introducing how graphs can be used in feature engineering, and the Label Propagation algorithm, which uses message passing on a graph. Discord server: https://discord.gg/xh2
From playlist Graph Neural Networks
Determining Signal Similarities
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox™. For more on Signal Processing To
From playlist Signal Processing and Communications
Introduction to Labels | Marketing Analytics for Beginners | Part-9
In Google Analytics, labels are defined as phrases or names applied to a person or a thing. They help determine the persona of a customer, allowing companies to divide a collective audience into segments. This division helps companies create customized and targeted advertisements to entice
From playlist Marketing Analytics for Beginners
Networks - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
My attempt at writing an epidemic algorithm to describe the propagation of viral videos... -- Watch live at https://www.twitch.tv/simuleios
From playlist Viral videos
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Determine the period of a signal by measuring the distance between the peaks, and find peaks in a noisy signal using Signal Processing Toolbox™. For more on Signal Process
From playlist Signal Processing and Communications
Star Network - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Network Analysis. Lecture 17 (part 1). Label propagation on graphs.
Node labeling. Label propagation. Iterative classification. Semi-supervised learning. Regularization on graphs Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture17.pdf
From playlist Structural Analysis and Visualization of Networks.
Network Analysis. Lecture 17 (part 2). Label propagation on graphs.
Node labeling. Label propagation. Iterative classification. Semi-supervised learning. Regularization on graphs Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture17.pdf
From playlist Structural Analysis and Visualization of Networks.
Graph Data Structure 5. Dijkstra’s Shortest Path Implementation in VB.NET
This is the fifth in a series of videos about the graph data structure. It explains how Dijkstra’s shortest path algorithm can be implemented for a weighted graph in VB.NET. This particular implementation involves coding up a Dijkstra class, whose constructor is passed a graph’s adjacenc
From playlist Path Finding Algorithms
This Algorithm Could Make a GPT-4 Toaster Possible
The Forward-Forward algorithm from Geoffry Hinton is a backpropagation alternative inspired by learning in the cortex. It tackles several issues with backprop that would allow it to be run much more efficiently. Hopefully research like this continues to pave the way toward full-hardware in
From playlist Data Science
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures (Paper Explained)
Backpropagation is one of the central components of modern deep learning. However, it's not biologically plausible, which limits the applicability of deep learning to understand how the human brain works. Direct Feedback Alignment is a biologically plausible alternative and this paper show
From playlist Papers Explained
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs (Paper Explained)
#ai #biology #neuroscience Backpropagation is the workhorse of modern deep learning and a core component of most frameworks, but it has long been known that it is not biologically plausible, driving a divide between neuroscience and machine learning. This paper shows that Predictive Codin
From playlist Papers Explained
Nexus Trimester - Sewoong Oh (UIUC)
Near-optimal message-passing algorithms for crowdsourcing Sewoong Oh (UIUC) March 17, 2016 Abstract: Crowdsourcing systems, like Amazon Mechanical Turk, provide platforms where large-scale projects are broken into small tasks that are electronically distributed to numerous on-demand cont
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
LambdaConf 2015 - Introduction to Constraint Logic Programming Sergii Dymchenko
Constraint logic programming is a paradigm that allows solving hard combinatorial problems with minimal programming effort. In this workshop you will learn the basics of the Prolog-based constraint logic programming system ECLiPSe, solve several puzzles, and get hints how constraint logic
From playlist LambdaConf 2015
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions (Paper Explained)
#imle #backpropagation #discrete Backpropagation is the workhorse of deep learning, but unfortunately, it only works for continuous functions that are amenable to the chain rule of differentiation. Since discrete algorithms have no continuous derivative, deep networks with such algorithms
From playlist Papers Explained
But what *is* a Neural Network? - THE MATH YOU SHOULD KNOW!
We'll take a look at how exactly neural networks learn by starting with modeling an objective function through Maximum Likelihood Estimation. We then take a look at neural network training using back propagation and Stochastic gradient descent. FOLLOW ME https://www.quora.com/profile/Ajay
From playlist The Math You Should Know
In this HTML video, you’ll learn about paragraphs. They help to organize text on websites. We hope you enjoy! To learn more, check out our Basic HTML tutorial here: https://edu.gcfglobal.org/en/basic-html/ #html #htmlparagraphs #coding
From playlist HTML
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 9 - Deep Reinforcement Learning
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University http://onlinehub.stanford.edu/ Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanfo
From playlist Stanford CS230: Deep Learning | Autumn 2018