Queueing theory | Application-specific graphs
In queueing theory, a loss network is a stochastic model of a telephony network in which calls are routed around a network between nodes. The links between nodes have finite capacity and thus some calls arriving may find no route available to their destination. These calls are lost from the network, hence the name loss networks. The loss network was first studied by Erlang for a single telephone link. Frank Kelly was awarded the Frederick W. Lanchester Prize for his 1991 paper Loss Networks where he demonstrated the behaviour of loss networks can exhibit hysteresis. (Wikipedia).
Staysafe.org: Protect your computer
The Internet is a global network that connects us to limitless information and opportunities. But there are risks involved with connecting to the Internet, such as downloading viruses and spyware onto computers and devices. Watch this video for four easy steps to help protect your computer
From playlist awareness
American Blackout | National Geographic
A cyber-attack takes down the grid and leaves millions of ordinary people without electricity. Follow the first-hand stories of five different groups stranded in the darkness as their desperate scenarios unfold. What happens when the lights go out? How long would it take for civilization t
From playlist American Blackout | National Geographic
WIFI disappears after sleep fix for laptop notebook computer wireless Wi-Fi
Recently I found that after I put my computer to sleep and wake it again, I can't connect to the wireless network anymore, so I have to restart the computer to connect to the wireless network. Here is a video on how I fixed it.
From playlist Technology
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
From playlist Communications & Network Systems
If you don't use a VPN for privacy on the Web (or have family members who don't), this video is my 10-minute attempt to convince you that it's worth the trouble. TOC in description. 1:23 - Private information about you that your ISP (comcast, verizon) can sell 2:54 - What is a VPN? Common
From playlist Bite-Sized Knowledge
Everything is Going Wrong | American Blackout
It's day two of the blackout. Food and water supplies quickly become an issue. ➡ Subscribe: http://bit.ly/NatGeoSubscribe About National Geographic: National Geographic is the world's premium destination for science, exploration, and adventure. Through their world-class scientists, pho
From playlist American Blackout | National Geographic
YouTube Has Blacklisted My Channel
YouTube is refusing to suggest my content to my subscribers and wider audiences. This can't go on. If you value my videos, please support my independent work on: https://www.patreon.com/thehatedone I have never trusted YouTube. I have always considered it a necessary evil for this channe
From playlist All of my videos
This Is The History Of The Internet | Mach | NBC News
There is no modern world without the Internet. An idea as much an actual invention, the Internet has changed and informed nearly every aspect of our lives. Now the question is: what comes next? » Subscribe to NBC News: http://nbcnews.to/SubscribeToNBC » Watch more NBC video: http://bit.ly/
From playlist Global Security, Then and Now
Tom Goldstein: "What do neural loss surfaces look like?"
New Deep Learning Techniques 2018 "What do neural loss surfaces look like?" Tom Goldstein, University of Maryland Abstract: Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well known that certain network architecture desi
From playlist New Deep Learning Techniques 2018
A Hands-on Introduction to Physics-informed Machine Learning
2021.05.26 Ilias Bilionis, Atharva Hans, Purdue University Table of Contents below. This video is part of NCN's Hands-on Data Science and Machine Learning Training Series which can be found at: https://nanohub.org/groups/ml/handsontraining Can you make a neural network satisfy a physical
From playlist ML & Deep Learning
Knowledge Distillation - Keras Code Examples
This Keras Code Examples show you how to implement Knowledge Distillation! Knowledge Distillation has lead to new advances in compression, training state of the art models, and stabilizing Transformers for Computer Vision. All you need to do to build on this is swap out the Teacher and Stu
From playlist Keras Code Examples
Gradient Origin Networks (Paper Explained w/ Live Coding)
Neural networks for implicit representations, such as SIRENs, have been very successful at modeling natural signals. However, in the classical approach, each data point requires its own neural network to be fit. This paper extends implicit representations to an entire dataset by introducin
From playlist Papers Explained
Backpropagation And Gradient Descent In Neural Networks | Neural Network Tutorial | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=BackPropagationandGradientDescent-odlgtjXduVg&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Lea
From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn
The StatQuest Introduction to PyTorch
PyTorch is one of the most popular tools for making Neural Networks. This StatQuest walks you through a simple example of how to use PyTorch one step at a time. By the end of this StatQuest, you'll know how to create a new neural network from scratch, make predictions and graph the output,
From playlist StatQuest
Paris Perdikaris: "Overcoming gradient pathologies in constrained neural networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Overcoming gradient pathologies in constrained neural networks" Paris Perdikaris - University of Penns
From playlist Machine Learning for Physics and the Physics of Learning 2019
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University https://stanford.io/3eJW8yT Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanford.
From playlist Stanford CS230: Deep Learning | Autumn 2018
Backpropagation In Neural Networks | Backpropagation Algorithm Explained For Beginners | Simplilearn
This video on Backpropagation in Neural Networks will cover how backpropagation and gradient descent play a role in training neural networks. You will learn this using an example of how to recognize handwritten digits using a neural network. After predicting the results, you will see how t
From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn
Noether Networks: Meta-Learning Useful Conserved Quantities (w/ the authors)
#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet! Encoding inductive biases has been a long established methods to provide deep networks with the ability to learn from less data. Especially useful are encodings of symmetry properties of the
From playlist Papers Explained
From playlist Week 9: Social Networks