In network science, the efficiency of a network is a measure of how efficiently it exchanges information and it is also called communication efficiency. The underlying idea (and main assumption) is that the more distant two nodes are in the network, the less efficient their communication will be. The concept of efficiency can be applied to both local and global scales in a network. On a global scale, efficiency quantifies the exchange of information across the whole network where information is concurrently exchanged. The local efficiency quantifies a network's resistance to failure on a small scale. That is the local efficiency of a node characterizes how well information is exchanged by its neighbors when it is removed. (Wikipedia).
Network Security, Part 1 : Basic Encryption Techniques
Fundamental concepts of network security are discussed. It provides a good overview of secret Key and public key Encryption. Important data encryption standards are presented.
From playlist Network Security
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
Computer Literacy - (unit 4) - the internet - 1 of 4
Forth unit of a series for newbie computer users. See http://proglit.com/computer-skills/ for additional information and material.
From playlist Computer Literacy - (unit 4) - the internet
From playlist Communications & Network Systems
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 The Internet
Network Security: Classical Encryption Techniques
Fundamental concepts of encryption techniques are discussed. Symmetric Cipher Model Substitution Techniques Transposition Techniques Product Ciphers Steganography
From playlist Network Security
Theoretical Computer Science and Economics - Tim Roughgarden
Lens of Computation on the Sciences - November 22, 2014 Theoretical Computer Science and Economics - Tim Roughgarden, Stanford University Theoretical computer science offers a number of tools to reason about economic problems in novel ways. For example, complexity theory sheds new light
From playlist Lens of Computation on the Sciences
Kaggle Reading Group: EfficientNet (Part 2) | Kaggle
This week we'll be starting EfficientNet (Tan & Le, 2019), which was published at ICML 2019. The paper proposes a new family of models that are both smaller and faster to train than traditional convolutional neural networks. Link to paper: http://proceedings.mlr.press/v97/tan19a/tan19a.pd
From playlist Kaggle Reading Group | Kaggle
AI Weekly Update - November 25th, 2019 (#13)
https://arxiv.org/pdf/1911.09070.pdf http://josh-tobin.com/assets/pdf/randomization_and_the_reality_gap.pdf https://arxiv.org/pdf/1911.08265.pdf https://openai.com/blog/safety-gym/ https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html https://clvrai.github.io/furni
From playlist AI Research Weekly Updates
Basic Concepts in Number Theory & Finite Fields: Part 1
It covers Euclid's Algorithm, Euclid's Algorithm: Tabular Method, Modular Arithmetic, Modular Arithmetic Operations, Modular Arithmetic Properties, Group, Cyclic Group, Ring, Field, Finite Fields or Galois Fields, Polynomial Arithmetic, Polynomial Arithmetic with Mod 2 Coefficients.
From playlist Network Security
SDS 473: Machine Learning at NVIDIA — with Anima Anandkumar
Anima Anandkumar joins us to discuss her work as a researcher in machine learning at NVIDIA and a professor at CalTech, and how they often go hand-in-hand and inform each other. In this episode you will learn: • Anima’s recent discovery of yoga [2:37] • How does Anima balance her work? [9
From playlist Super Data Science Podcast
Computer Literacy - (unit 4) - the internet - 2 of 4
Forth unit of a series for newbie computer users. See http://proglit.com/computer-skills/ for additional information and material.
From playlist Computer Literacy - (unit 4) - the internet
Kaggle Reading Group: EfficientNet | Kaggle
This week we'll be starting EfficientNet (Tan & Le, 2019), which was published at ICML 2019. The paper proposes a new family of models that are both smaller and faster to train than traditional convolutional neural networks. Link to paper: http://proceedings.mlr.press/v97/tan19a/tan19a.pd
From playlist Kaggle Reading Group | Kaggle
Ross King: "Automating Science using Robot Scientists"
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Automating Science using Robot Scientists" Ross King, University of Manchester Institute of Science and Technology (UMIST) Abstract: A Rob
From playlist Machine Learning for Physics and the Physics of Learning 2019
Steven Low - CS+Energy - Alumni College 2016
"Greening the Grid through Optimization and Control" Steven Low, Professor of Computer Science and Electrical Engineering, is deeply involved in research with information and energy infrastructure, which have completely changed the way we live and work since their overlapping inceptions ab
From playlist Talks and Seminars
Aiichiro Nakano - Quantum Material Dynamics at Nexus of Exascale Computing, AI, & Quantum Computing
Recorded 27 March 2023. Aiichiro Nakano of the University of Southern California presents "Quantum Materials Dynamics at the Nexus of Exascale Computing, Artificial Intelligence, and Quantum Computing" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
DDPS | Towards automatic architecture design for emerging machine learning tasks | Misha Khodak
Hand-designed neural networks have played a major role in accelerating progress in traditional areas of machine learning such as computer vision, but designing neural networks for other domains remains a challenge. Successfully transferring existing architectures to applications such as se
From playlist Data-driven Physical Simulations (DDPS) Seminar Series