Evolving networks are networks that change as a function of time. They are a natural extension of network science since almost all real world networks evolve over time, either by adding or removing nodes or links over time. Often all of these processes occur simultaneously, such as in social networks where people make and lose friends over time, thereby creating and destroying edges, and some people become part of new social networks or leave their networks, changing the nodes in the network. Evolving network concepts build on established network theory and are now being introduced into studying networks in many diverse fields. (Wikipedia).
The visual evolution of the Internet
The Internet has changed a lot over the past few decades. Here's a look at some of the ways the Web has evolved—from the first Web site to dial-up connections to modern day social networking sites.
From playlist The Internet
An intro to the core protocols of the Internet, including IPv4, TCP, UDP, and HTTP. Part of a larger series teaching programming. See codeschool.org
From playlist The Internet
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Networking
Networks: An example of a Network
We're busy people who learn to code, then practice by building projects for nonprofits. Learn Full-stack JavaScript, build a portfolio, and get great references with our open source community. Join our community at https://freecodecamp.com Follow us on twitter: https://twitter.com/freecod
From playlist Networks
We're busy people who learn to code, then practice by building projects for nonprofits. Learn Full-stack JavaScript, build a portfolio, and get great references with our open source community. Join our community at https://freecodecamp.com Follow us on twitter: https://twitter.com/freecod
From playlist Networks
How I created an evolving neural network ecosystem
After my last video I got a lot of comments (mainly on Reddit) asking me to make a video explaining how I did it. It took me a while to learn how to video edit, voice act, and animate, so it was about time I presented and explained this project. The Bibites Made in C# on Unity I highly
From playlist Progress of Artificial Life Simulation
20: Hopfield Networks - Intro to Neural Computation
MIT 9.40 Introduction to Neural Computation, Spring 2018 Instructor: Michale Fee View the complete course: https://ocw.mit.edu/9-40S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61I4aI5T6OaFfRK2gihjiMm Covers recurrent networks with lambda greater than one, attract
From playlist MIT 9.40 Introduction to Neural Computation, Spring 2018
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-01-20 Discussion lead: Maja Maher Discussion facilitator(s): Susan Shu Chang
From playlist Architecture Tuning
Network evolution in Immune system and Development (Lecture - 03) by Paul François
Winter School on Quantitative Systems Biology DATE:04 December 2017 to 22 December 2017 VENUE:Ramanujan Lecture Hall, ICTS, Bengaluru The International Centre for Theoretical Sciences (ICTS) and the Abdus Salam International Centre for Theoretical Physics (ICTP), are organizing a Winter S
From playlist Winter School on Quantitative Systems Biology
DDPS | Learning to accelerate large-scale physical simulations in fluid and plasma physics
Description: Simulating the time evolution of large-scale physical systems is crucial in many scientific and engineering domains, such as in fluid dynamics and plasma physics. Typically, domain-specific classical numerical solvers are employed to simulate such systems, which may require ma
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
A.I. Learns to Play Sonic the Hedgehog - NEAT Explained!
After the struggles I faced coding my own NEAT algorithm to beat the game Breakout, I've decided to take a step back and research what makes a successful NEAT algorithm by using OpenAI Gym Retro and a pure Python implementation of NEAT called NEAT-Python. I've used a Python script to conne
From playlist Artificial Intelligence & Machine Learning
Bitcoin Protocol Tutorial #8: Incentive
Bitcoin Protocol Paper Playlist: http://www.youtube.com/watch?v=UieiMU-ImvI&list=PLQVvvaa0QuDcq2QME4pfeh0cE71mkb_qz&feature=share All Bitcoin Videos Playlist: http://www.youtube.com/watch?v=UieiMU-ImvI&feature=share&list=PLQVvvaa0QuDebbCxrDPCux6SzC1RET4mF In this Bitcoin protocol paper t
From playlist Bitcoin Satoshi Paper Explained
Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
Joint work with Nathan Kutz: https://www.youtube.com/channel/UCoUOaSVYkTV6W4uLvxvgiFA Discovering physical laws and governing dynamical systems is often enabled by first learning a new coordinate system where the dynamics become simple. This is true for the heliocentric Copernican syste
From playlist Data-Driven Dynamical Systems with Machine Learning
Random Walks on Dynamic Graphs by John Augustine
Games, Epidemics and Behavior URL: http://www.icts.res.in/discussion_meeting/geb2016/ DATES: Monday 27 Jun, 2016 - Friday 01 Jul, 2016 VENUE : Madhava lecture hall, ICTS Bangalore DESCRIPTION: The two main goals of this Discussion Meeting are: 1. To explore the foundations of policy d
From playlist Games, Epidemics and Behavior
Poisson random fields for dynamic feature models: Valerio Perrone, Oxford-Warwick Stats Programme
This talk is based on the article: http://jmlr.org/papers/volume18/16-541/16-541.pdf In a feature allocation model, each data point depends on a collection of unobserved latent features. For example, we might classify a corpus of texts by describing each document via a set of topics; the
From playlist Turing Seminars
This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
DDPS | Distilling nonlinear shock waves
Classical reduced models rely on low-dimensional linear subspaces that are able to represent the solution manifold. For transport dominated problems, a suitable subspace does not exist, due to the slow decay in the Kolmogorov N-width of the solution manifold. In this talk, I will present d
From playlist Data-driven Physical Simulations (DDPS) Seminar Series