Consensus clustering

Clustering 1: Overview

From playlist Clustering Algorithms

Introduction to Clustering

We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning technique that consists of grouping data points and creating partitions based on similarity. The ultimate goal is to find groups of simila

From playlist Data Science in Minutes

IAML2.5: What is clustering?

From playlist Thinking about Data

Clustering 9: Evaluating clustering systems

From playlist Clustering Algorithms

Hierarchical Clustering 5: summary

[http://bit.ly/s-link] Summary of the lecture.

From playlist Hierarchical Clustering

Clustering Introduction - Practical Machine Learning Tutorial with Python p.34

In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scient

From playlist Machine Learning with Python

Clustering (2): Hierarchical Agglomerative Clustering

Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.

From playlist cs273a

Clustering Coefficient - 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

Sebastian Will: Comparative methods for RNA structure analysis - Lecture 2

Recording during the meeting "AlgoSB 2019 - Mathematical and Computational Methods for Structured RiboNucleic Acids" the January 17, 2019 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by w

From playlist Mathematics in Science & Technology

Rule #2: Double Tap. An Elasticsearch Journey of Resiliency - George Kobar - REdeploy 2019

At Elastic, our goal is to continuously improve upon the resiliency of Elasticsearch and our other open source software. With each new feature or improvement has brought a new set of resiliency challenges and unintended consequences. Including creating zombies and split brain applications/

From playlist REdeploy 2019

Ruby Conf 2013 - Raft: Consensus for Rubyists by Patrick Van Stee

Consensus. It's not anything new. In fact, there have been great papers and discussions on the topic since the late 80's. But this year, a new consensus algorithm was written up in a paper titled, "In Search of an Understandable Consensus Algorithm". This is actually pretty rare in academi

From playlist RubyConf 2013

GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)

#glom #hinton #capsules Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs. GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previo

From playlist Papers Explained

Mary Scott - Supervised and Unsupervised approaches for Electron Microscopy Data Analysis

Recorded 01 December 2022. Mary Scott of the University of California, Berkeley, presents "Supervised and Unsupervised approaches for Electron Microscopy Data Analysis" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Recently, materials science has undergo

From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling

DEFCON 13: Reverse Engineering Network Protocols using Bioinformatics

Speaker: Marshall Beddoe Network protocol analysis is currently performed by hand using only intuition and a protocol analyzer tool such as tcpdump or Ethereal. This talk presents Protocol Informatics, a method for automating network protocol reverse engineering by utilizing algorithms f

From playlist DEFCON 13

IAML19.7 Summary

From playlist Hierarchical Clustering

Benedetto Piccoli: "Beyond Vehicles and Other Flow Systems"

Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Beyond Vehicles and Other Flow Systems" Benedetto Piccoli - Rutgers University-Camden Institute for Pure and Applied Mathematics, UCLA September 25, 2020 For more information: https://www.ipam.ucla.edu/avt

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

RubyConf 2021 - Learning Ractor with Raft by Micah Gates

Ractor is a new concurrency mechanism in Ruby that unlocks great performance improvement possibilities. Its well defined sharing and messaging system makes it easier to reason about than threaded models. Raft is a distributed consensus algorithm that backs distributed data stores such as

From playlist RubyConf 2021

Dark Matter Exists. Here's how we know.

Dark matter is 84% of the matter in the universe and it single-handedly explains a lot of stuff: cluster motion, galactic rotation, gravitational lensing, and the CMB. We have piles of irrefutable evidence. Let's go through it, shall we. Nick Lucid - Host/Writer/Editor/Animator Vanessa R

From playlist Dark Matter

Networks: Part 5 - Oxford Mathematics 4th Year Student Lecture

Network Science provides generic tools to model and analyse systems in a broad range of disciplines, including biology, computer science and sociology. This course (we are showing the whole course over the next few weeks) aims at providing an introduction to this interdisciplinary field o

From playlist Oxford Mathematics Student Lectures - Networks

Introduction to Hierarchical Clustering with College Scorecard Data

Clustering is an unsupervised machine learning technique where data need not be labeled. The goal of clustering is to find like-items such as similar customers, similar products, or similar students, just to name a few. Popular clustering algorithms include K-means and hierarchical cluster

From playlist Fundamentals of Machine Learning