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Graph Theory - An Introduction!
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Graph Theory - An Introduction! In this video, I discuss some basic terminology and ideas for a graph: vertex set, edge set, cardinality, degree of a vertex, isomorphic graphs, adjacency lists, adjacency matrix, trees and circuits. There is a MISTAKE on the adjacency matrix; I put a 1 in the v5 row and v5 column, but it should be placed in the v5 row and the v6 column. There are annotations pointing this out along with the corrected matrix!
Просмотров: 416529 patrickJMT
Graph theory complete tutorial  - Part #1
 
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Graph theory complete tutorial - Part #1: This video is the first part of the session of graph theory from edunic. graph theory is an important topic in circuit analysis and could be necessary in some circuits where conventional analysis techniques could not serve its purpose well. This tutorial talks all about the basic concepts in graph theory including how to draw the graph, the trees, twigs and links. Later on , we solve problems using the important matrices in graph theory namely, the incidence matrix, tie set matrix and the cut set matrix. topics like V-shift and I-shift is also briefly explained at the end of the session. graph theory lectures, graph theory discrete mathematics, graph theory circuit and system, graph theory nptel, graph theory lectures in hindi, graph theory in network analysis, graph theory discrete mathematics in hindi, graph theory computer science, graph theory lectures for gate, graph theory for gate, graph theory, graph theory in hindi, graph theory and combinatorics, graph theory algorithms, graph theory and applications, graph theory and network equations, graph theory and network topology, graph theory and trees, graph theory and probability, graph theory adjacency, graph theory khan academy, graph theory network analysis, graph theory by ravindrababu ravula, graph theory basics, graph theory by sarada herke, graph theory by ks academy, graph theory by jaipal, graph theory by ravindrababu, graph theory bfs, graph theory basic concepts, graph theory bipartite, graph theory breadth first search, graph theory cut set, graph theory coloring, graph theory circuit, graph theory connectivity, graph theory computer science in hindi, graph theory course, graph theory concept of tree, graph theory cs, graph theory data structure, graph theory discrete mathematics nptel, graph theory discrete maths, graph theory dfs, graph theory discrete mathematics for gate, graph theory definitions, graph theory duality, graph theory degree, graph theory electrical, graph theory electrical networks, graph theory examples, graph theory euler paths and euler circuits, graph theory euler, graph theory explained, euler's formula graph theory, extremal graph theory, elementary graph theory, graph theory for computer science, graph theory for ugc net, graph theory for educators, graph theory flow, graph theory gate, graph theory gate lectures, graph theory gate cse, graph theory gate questions, graph theory girth, graph theory complete graph, graph theory hindi, graph theory hamiltonian, graph theory history, graph theory sarada herke, graph theory in discrete mathematics, graph theory in discrete mathematics in hindi, graph theory in data structure, graph theory in circuit theory, graph theory in circuit and system, graph theory in mfcs, graph theory introduction, graph theory in electrical circuits, graph theory java, graph theory ks academy, graph theory lectures nptel, graph theory lectures by ravindrababu ravula, graph theory lectures mit, graph theory lecture 1, graph theory lectures in hindi gate, graph theory loop, graph theory linear algebra, graph theory mit, graph theory mathematics, graph theory matching, graph theory maths, graph theory mfcs, graph theory mycodeschool, graph theory mathematics lectures in hindi, graph theory matrices, graph theory mathispower4u, graph theory matlab, graph theory neso academy, graph theory nptel lectures, graph theory nptelhrd, graph theory neso, graph theory numericals, graph theory nas, graph theory networks, graph theory network analysis nptel, graph theory optimization, lectures on graph theory, applications of graph theory, theory of graph, mit opencourseware graph theory, graph theory problems, graph theory programming, graph theory patrickjmt, graph theory proofs, graph theory prim's algorithm, graph theory path, graph theory planar graph, graph theory python, graph theory puzzles, graph theory playlist, graph theory questions, graph theory ravindrababu, graph theory shortest path, graph theory spanning tree, graph theory stanford, graph theory solved problems, graph theory subgraphs, graph theory saurabhschool, spectral graph theory, cut set graph theory, graph theory tutorial, graph theory trees, graph theory terminology, graph theory theorems, graph theory tushar roy, graph theory tutorial in hindi, graph theory terminology in hindi, graph theory ted, graph theory tournament, introduction to graph theory, graph theory udacity, graph theory vtu, graph theory video lectures, graph theory videos, graph theory walk, what is graph theory,
Просмотров: 17478 eduNIC education
Basic Concepts of Networks | Network Theory
 
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Subject- Network Theory Topic - Basic Concepts of Networks Faculty- Mr. Umesh Dhande sir GATE ACADEMY is the India"s leading institute with best teaching practices and most affordable fee. This video will help you to understand the Basic Concepts of Networks Dear viewer, if you have any doubt on this topic join our interactive session at our FB group to have a direct interaction with our faculties & clarify your doubts. Doubt solving facebook group - https://www.facebook.com/groups/gateconcepts Check out our web & facebook page for more details. 1. Web: www.gateacademy.co.in 2. Email: info@gateacademy.co.in 3. For any doubt join us :https://www.facebook.com/groups/gateconcepts 4. Like us: https://www.facebook.com/gateacademyofficial 5. Instagram - gate_academybhilai Do not Forget to watch the videos of below mentioned subjects : 1. Analog Electronics - https://goo.gl/UFxDKV 2. Engineering Mathematics - https://goo.gl/9rLYLq 3. Signal & System - https://goo.gl/rDfWjC 4. Communication System - https://goo.gl/rDfWjC 5. Analog Electronics | Live Classroom - https://goo.gl/gZUDdK 6. Electrical & Electronic Measurement - https://goo.gl/uZJhES 7. Control Systems - https://goo.gl/fsuwGV 8. Electromagnetic Theory (EMT) - https://goo.gl/ToVDgU 9. Digital Electronics - https://goo.gl/TUR8Fj 10.Network Theory - https://goo.gl/bcNKLB 11.Unfiltered Conversation - https://goo.gl/XziCfi
Просмотров: 121443 GATE ACADEMY
Analysis of Microwave Networks
 
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Subscribe to Unacademy Engineering Students here-: https://goo.gl/UGFo7b Analysis of Microwave Networks A microwave network is formed when several microwave devices are connected together by transmission lines for transmission of microwave signals. In this video Pooja explains the Analysis of Microwave Networks You can find the entire course here-: https://goo.gl/J6uHM7 You can find all the courses by Pooja -: https://goo.gl/21e4R Download the Unacademy Learning App from the Google Play Store here:- https://goo.gl/02OhYI Download the Unacademy Educator app from the Google Play Store here: https://goo.gl/H4LGHE Do Subscribe and be a part of the community for more such lessons here: https://goo.gl/UGFo7b For more awesome courses on engineering visit: Visit Our Facebook Group on Engineering Curriculum here: https://goo.gl/5EqfqS
Просмотров: 201 Unacademy - Engineering Students
Network Theory Lec-11-2: Nodal Analysis
 
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In this lecture, a very important concept Nodal Analysis of network theory subject is discussed. For lecture Material: https://learningzeverything.blogspot.in/
Просмотров: 7058 Learning Is Everything
Graph properties of transcription networks
 
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MIT 8.591J Systems Biology, Fall 2014 View the complete course: http://ocw.mit.edu/8-591JF14 Instructor: Jeff Gore Prof. Jeff Gore continues the discussion of oscillators, including alternative designs for oscillators. He then discusses the article Emergence of scaling in random networks, by Barabási & Albert. The final topic is network motifs. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Просмотров: 2617 MIT OpenCourseWare
End of Season Elimination: Details
 
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Use of network flow to determine which teams have been eliminated in baseball.
Просмотров: 1961 UC Davis Academics
Understanding your brain as a network and as art | Danielle Bassett | TEDxPenn
 
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How do connectivity patterns inside of your brain change when you learn a new skill? Danielle Bassett seeks to uncover this complexity and develop treatments for neurological diseases with math—and art. As the youngest recipient of the 2014 MacArthur Award Genius Grant and an Assistant Professor of Innovation in the University of Pennsylvania's Department of Bioengineering, Danielle Bassett derives mathematical models from aesthetic representations of brain networks. She aims to diagram the organizational principles in the human brain and understand how brain networks can be reconfigured to quickly incorporate new information and enhance learning, tackling seemingly unsolvable problems with grace and confidence. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Просмотров: 12179 TEDx Talks
DeepWalk: Turning Graphs Into Features via Network Embeddings
 
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Dr. Steven Skiena, Stony Brook University Michael Hunger, Neo4j Random walk algorithms help better model real-world scenarios, and when applied to graphs, can significantly improve machine learning. Learn how the Deepwalk supervised learning algorithm transfers deep learning techniques from natural language processing to network analysis, and explore the motivations behind graph-enhanced machine learning. #MachineLearning #DeepWalk #NLP
Просмотров: 408 Neo4j
We are all nodes, living in the network: Alessandro Zonin at TEDxVerona
 
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Seguici su Facebook: http://www.facebook.com/TEDxVerona Alessandro Zonin è Sociologo di formazione, lavora come Marketing Manager presso una nota multinazionale americana nel settore dell'Informazione Technology. Da sempre ama raccontare storie con i dati si è occupato, tra le altre cose, di progettare sistemi di business intelligence e data analytics basati su prodotti di mercato e open source. Si laurea nel 2009 in Sociologia a Trento, con un percorso di studio incentrato su Lavoro, Organizzazione e Sistemi Informativi. Ha partecipato a numerosi workshop e come relatore a vari eventi e corsi in Italia e all'estero con diverse pubblicazioni. Collabora attivamente con Università, associazioni ed enti di ricerca sui temi della Social Network Analysis delle Smart city e del Digital Journalism. È il primo italiano certificato come Verified Gephi Trainer. Vive a Verona e lavora tra Milano e Verona. In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Просмотров: 1305 TEDx Talks
Use forward and backward pass to determine project duration and critical path
 
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Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: Use forward and backward pass to determine project duration and critical path.
Просмотров: 660765 Engineer4Free
CPM - Critical Path Method||Project Management Techniques||Operations Research|| Solved Problem
 
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▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 7401428918 * Paypal a/c : www.paypal.me/kauserwisetutorial [Every contribution is helpful] Thanks & All the Best!!! ─────────────────────────── Please watch this video to understand full CPM concept, and I have given the necessary theory explanation with an example problem, Project Management - CPM [Critical Path Method] Critical Path Network, Project completion time, Total floats and Free floats in simple explanation. How to construct CPM network? What is Project management - cpm? What is forward pass and backward pass in CPM? What is Earliest time and latest completion time? How to find Free floats and total floats? To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research Playlists: For Financial accounting - https://www.youtube.com/playlist?list=PLabr9RWfBcnojfVAucCUHGmcAay_1ov46 For Cost and Management accounting - https://www.youtube.com/playlist?list=PLabr9RWfBcnpgUjlVR-znIRMFVF0A_aaA For Corporate accounting - https://www.youtube.com/playlist?list=PLabr9RWfBcnorJc6lonRWP4b39sZgUEhx For Operations Research - https://www.youtube.com/playlist?list=PLabr9RWfBcnoLyXr4Y7MzmHSu3bDjLvhu
Просмотров: 1225935 Kauser Wise
How to Utilise Test Series ?
 
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GATE ACADEMY is the India's leading institute with best teaching practices and most affordable fee. Dear viewer, if you have any doubt on this topic join our interactive session at our FB group to have a direct interaction with our faculties & clarify your doubts. Doubt solving facebook group - https://www.facebook.com/groups/gateconcepts Check out our web & facebook page for more details. 1. Web: www.gateacademy.co.in 2. Email: info@gateacademy.co.in 3. For any doubt join us :https://www.facebook.com/groups/gateconcepts 4. Like us: https://www.facebook.com/gateacademyofficial 5. Instagram - gate_academybhilai Do not Forget to watch the videos of below mentioned subjects : 1. Analog Electronics - https://goo.gl/UFxDKV 2. Engineering Mathematics - https://goo.gl/9rLYLq 3. Signal & System - https://goo.gl/rDfWjC 4. Communication System - https://goo.gl/rDfWjC 5. Analog Electronics | Live Classroom - https://goo.gl/gZUDdK 6. Electrical & Electronic Measurement - https://goo.gl/uZJhES 7. Control Systems - https://goo.gl/fsuwGV 8. Electromagnetic Theory (EMT) - https://goo.gl/ToVDgU 9. Digital Electronics - https://goo.gl/TUR8Fj 10.Network Theory - https://goo.gl/bcNKLB 11.Unfiltered Conversation - https://goo.gl/XziCfi
Просмотров: 9938 GATE ACADEMY
How to draw a CPM network diagram
 
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Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: How to draw a CPM network diagram.
Просмотров: 174643 Engineer4Free
Statistical Modeling of Social Networks
 
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1/28/2013 Mark Handcock - UCLA "Statistical Modeling of Social Networks" "In this talk we give an overview of social network analysis from the perspective of a statistician. The networks field is, and has been, broadly multidisciplinary with significant contributions from the social, natural and mathematical sciences. This has lead to a plethora of terminology, and network conceptualizations commensurate with the varied objectives of network analysis. As the primary focus of the social sciences has been the representation of social relations with the objective of understanding social structure, social scientists have been central to this development. We illustrate these ideas with Exponential-family random graph models (ERGM) which attempt to represent the complex dependencies in networks in a parsimonious, tractable and interpretable way. A major barrier to the application of such models has been lack of understanding of model behavior and a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the network structure produced by the models matches that of the data. We will also consider latent cluster random effects models and touch upon issues of the sampling of networks and partially-observed networks. We illustrate these methods using the "statnet" open-source software suite (http://statnet.org)."
Просмотров: 3788 UCLABEC
1. Network Theory- Preparation Strategy for GATE 2018/19 (EC)
 
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This is a preparation strategy for GATE 2018/19 specially for those who are appearing in exam for Electronics Engineering. This video explains the various do's and dont's a student must follow while preparing for his/her GATE attempt. Mr. Umesh Dhande explains subject wise approach to every subject and thus provides best guidelines to students. In this video he explains the important topics and strategy for subject "NETWORK THEORY". 1. Network Theory https://www.youtube.com/watch?v=UAgO7GW5Frs 2. Signal and System https://www.youtube.com/watch?v=KH3J6jGEUPg 3. Digital Electronics and 8085 https://www.youtube.com/watch?v=TutFY0P23pY 4. Control System https://www.youtube.com/watch?v=NiDNiwWbmwA 5. Electronic Devices https://www.youtube.com/watch?v=fj1Opkp02h8 6. Analog Electronics https://www.youtube.com/watch?v=bpIYfwJZceI 7. Electromagnetic Theory https://www.youtube.com/watch?v=Kb6pdgTRPEo 8. Communication System https://www.youtube.com/watch?v=OMhT_RbEazc 9. Engineering Mathematics and Aptitude https://www.youtube.com/watch?v=1hDEPbvyCMw For joining closed facebook group- https://www.facebook.com/groups/gateconcepts/ GATE Academy facebook page- https://www.facebook.com/gateacademy.official/ Online test series for gate- onlinetestseries.gateacademy.co.in For more such videos, lectures and tutorials subscribe to our youtube channel.
Просмотров: 59210 GATE ACADEMY
Lec-19 Network Models
 
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Lecture series on Advanced Operations Research by Prof. G.Srinivasan, Department of Management Studies, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Просмотров: 199245 nptelhrd
GraphConnect 2018 - Live from NYC: Hilary Mason and Emil Eifrem
 
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Live from Times Square in New York City, Neo4j's CEO Emil Eifrem will give the State of the Graph keynote -- talking about the latest advances in Neo4j and the graph ecosystem. Next up, Hilary Mason of Cloudera and Fast Forward Labs fame will talk about the Present and Future of Artificial Intelligence and Machine Learning. #GraphConnect #GraphDatabases #Neo4j
Просмотров: 4293 Neo4j
Bayesian Belief Network ll Directed Acyclic Graph and Conditional Probability Table Explained
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Просмотров: 48 5 Minutes Engineering
Electrical Networks: Voltages and Currents
 
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MIT RES.18-009 Learn Differential Equations: Up Close with Gilbert Strang and Cleve Moler, Fall 2015 View the complete course: http://ocw.mit.edu/RES-18-009F15 Instructor: Gilbert Strang Current flowing around an RLC loop solves a linear equation with coefficients L (inductance), R (resistance), and 1/C (C = capacitance). License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Просмотров: 13759 MIT OpenCourseWare
Andrew Rowan - Bayesian Deep Learning with Edward (and a trick using Dropout)
 
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Filmed at PyData London 2017 Description Bayesian neural networks have seen a resurgence of interest as a way of generating model uncertainty estimates. I use Edward, a new probabilistic programming framework extending Python and TensorFlow, for inference on deep neural nets for several benchmark data sets. This is compared with dropout training, which has recently been shown to be formally equivalent to approximate Bayesian inference. Abstract Deep learning methods represent the state-of-the-art for many applications such as speech recognition, computer vision and natural language processing. Conventional approaches generate point estimates of deep neural network weights and hence make predictions that can be overconfident since they do not account well for uncertainty in model parameters. However, having some means of quantifying the uncertainty of our predictions is often a critical requirement in fields such as medicine, engineering and finance. One natural response is to consider Bayesian methods, which offer a principled way of estimating predictive uncertainty while also showing robustness to overfitting. Bayesian neural networks have a long history. Exact Bayesian inference on network weights is generally intractable and much work in the 1990s focused on variational and Monte Carlo based approximations [1-3]. However, these suffered from a lack of scalability for modern applications. Recently the field has seen a resurgence of interest, with the aim of constructing practical, scalable techniques for approximate Bayesian inference on more complex models, deep architectures and larger data sets [4-10]. Edward is a new, Turing-complete probabilistic programming language built on Python [11]. Probabilistic programming frameworks typically face a trade-off between the range of models that can be expressed and the efficiency of inference engines. Edward can leverage graph frameworks such as TensorFlow to enable fast distributed training, parallelism, vectorisation, and GPU support, while also allowing composition of both models and inference methods for a greater degree of flexibility. In this talk I will give a brief overview of developments in Bayesian deep learning and demonstrate results of Bayesian inference on deep architectures implemented in Edward for a range of publicly available data sets. Dropout is an empirical technique which has been very successfully applied to reduce overfitting in deep learning models [12]. Recent work by Gal and Ghahramani [13] has demonstrated a surprising formal equivalence between dropout and approximate Bayesian inference in neural networks. I will compare some results of inference via the machinery of Edward with model averaging over neural nets with dropout training. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Просмотров: 17265 PyData
Vectors and 2D Motion: Crash Course Physics #4
 
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Continuing in our journey of understanding motion, direction, and velocity... today, Shini introduces the ideas of Vectors and Scalars so we can better understand how to figure out motion in 2 Dimensions. But what does that have to do with baseball? Or two baseballs? *** Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Help PBSDS win a Webby Award by voting here: https://pv.webbyawards.com/2016/online-film-video/video-channels-and-networks/science-education Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark, Eric Kitchen, Jessica Wode, Jeffrey Thompson, Steve Marshall, Moritz Schmidt, Robert Kunz, Tim Curwick, Jason A Saslow, SR Foxley, Elliot Beter, Jacob Ash, Christian, Jan Schmid, Jirat, Christy Huddleston, Daniel Baulig, Chris Peters, Anna-Ester Volozh, Ian Dundore, Caleb Weeks -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashC... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support CrashCourse on Patreon: http://www.patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Просмотров: 675170 CrashCourse
The Wonders of the Probabilistic Method
 
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I will try to explain some key principles in modern mathematics which combine ideas from combinatorics and probability. In particular I will emphasize the surprising role that probability theory plays in the study of combinatorics. How it allows us to investigate complicated graphs and networks without having to reveal all the specific details of individual large graphs or networks. This talk is intended for a general audience. The necessary mathematical background is at the level of good high-school education.
Просмотров: 905 Microsoft Research
Network Optimization Models
 
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Просмотров: 2889 Supply Chain Analytics
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
 
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Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Просмотров: 517292 MBAbullshitDotCom
Diffusion in Social and Information Networks - Part 2
 
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Authors: Manuel Gomez Rodriguez, Le Song Abstract: In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and granularity of large-scale diffusion data, which, in principle, allows for understanding and modeling not only macroscopic diffusion but also microscopic (node-level) diffusion. To this aim, a bottom-up approach has been typically considered, which starts by considering how particular ideas, pieces of information, products, or, more generally, contagions spread locally from node to node apparently at random to later produce global, macroscopic patterns at a network level.However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges which require leveraging methods from machine learning, probabilistic modeling, temporal point processes and graph theory, as well as the nascent field of network science. In this tutorial, we will present several diffusion models designed for fine-grained large-scale diffusion and social event data, present some canonical research problem in the context of diffusion, and introduce state-of-the-art algorithms to solve some of these problems, in particular, network estimation, influence estimation and control, and rumor source identification. ACM DL: http://dl.acm.org/citation.cfm?id=2789991 DOI: http://dx.doi.org/10.1145/2783258.2789991
Просмотров: 261 Association for Computing Machinery (ACM)
Summer learning - a shoutout to Khan academy asmr
 
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Просмотров: 912 yanghaiying
Linear transformations and matrices | Essence of linear algebra, chapter 3
 
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Matrices can be thought of as transforming space, and understanding how this work is crucial for understanding many other ideas that follow in linear algebra. Full series: http://3b1b.co/eola Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. http://3b1b.co/support ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Просмотров: 907459 3Blue1Brown
Network Theory Lec-1: Basics of Network Theory: charge, current, energy and voltage
 
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In this lecture, basics of networks like charge, current, energy and voltage are discussed. For Lecture Material: http://learningzeverything.blogspot.in/
Просмотров: 31839 Learning Is Everything
Single Phase  Uncontrolled Rectifier (Part - 1), Important GATE Questions | Power Electronics
 
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Subject- Power Electronics Topic - Single Phase Uncontrolled Rectifier Faculty- Mr. Umesh Dhande sir GATE ACADEMY is the India"s leading institute with best teaching practices and most affordable fee. This video will help you to understand the Single Phase Uncontrolled Rectifier Dear viewer, if you have any doubt on this topic join our interactive session at our FB group to have a direct interaction with our faculties & clarify your doubts. Doubt solving facebook group - https://www.facebook.com/groups/gateconcepts Check out our web & facebook page for more details. 1. Web: www.gateacademy.co.in 2. Email: info@gateacademy.co.in 3. For any doubt join us :https://www.facebook.com/groups/gateconcepts 4. Like us: https://www.facebook.com/gateacademyofficial 5. Instagram - gate_academybhilai Do not Forget to watch the videos of below mentioned subjects : 1. Analog Electronics - https://goo.gl/UFxDKV 2. Engineering Mathematics - https://goo.gl/9rLYLq 3. Signal & System - https://goo.gl/rDfWjC 4. Communication System - https://goo.gl/rDfWjC 5. Analog Electronics | Live Classroom - https://goo.gl/gZUDdK 6. Electrical & Electronic Measurement - https://goo.gl/uZJhES 7. Control Systems - https://goo.gl/fsuwGV 8. Electromagnetic Theory (EMT) - https://goo.gl/ToVDgU 9. Digital Electronics - https://goo.gl/TUR8Fj 10.Network Theory - https://goo.gl/bcNKLB 11.Unfiltered Conversation - https://goo.gl/XziCfi
Просмотров: 28178 GATE ACADEMY
What is Randomized Algorithm in Analysis of Algorithm
 
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Randomized Algorithm A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the ""average case"" over all possible choices of random bits. Formally, the algorithm's performance will be a random variable determined by the random bits; thus either the running time, or the output (or both) are random variables. Deterministic AlgorithmIn computer science, a deterministic algorithm is an algorithm which, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states. Deterministic algorithms are by far the most studied and familiar kind of algorithm, as well as one of the most practical, since they can be run on real machines efficiently. Formally, a deterministic algorithm computes a mathematical function; a function has a unique value for any input in its domain, and the algorithm is a process that produces this particular value as output. Types of Randomized Algorithms 1) Randomized Algorithm Las Veges Algorithms Output is always correct. Running time is a random variable . Example: Randomized quick Sort 2) Randomized Algorithm Monte Carlo Algorithms Output may be incorrect with some probability. Running time is deterministic. Example: Randomized algorithm for approximate mediam. Minimum cut In graph theory, a minimum cut of a graph is a cut (a partition of the vertices of a graph into two disjoint subsets that are joined by at least one edge) that is minimal in some sense. Primality Testing A primality test is a test to determine whether or not a given number is prime, as opposed to actually decomposing the number into its constituent prime factors (which is known as prime factorization). Primality tests come in two varieties: deterministic and probabilistic. #OnlineLectures #EducationForFree #FullHD #HappyLearning #Engineering Thanks For Supporting Us Website - http://ekeeda.com Parent Channel - https://www.youtube.com/c/ekeeda Facebook - https://www.facebook.com/ekeeda.video Twitter - https://twitter.com/Ekeeda_Official Blogger - http://ekeeda.blogspot.in Pinterest - https://in.pinterest.com/ekeedavideo Digg - http://digg.com/u/ekeeda_Video Tumbler - https://www.tumblr.com/blog/ekeedavideo Reddit - https://www.reddit.com/user/ekeeda_Video LinkedIn- https://www.linkedin.com/in/ekeeda-video-4a5b83124 Happy Learning : )
Просмотров: 807 Ekeeda
How to draw project S-curve for cumulative costs
 
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Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: How to draw project S-curve for cumulative costs.
Просмотров: 42759 Engineer4Free
Creating and Deploying CDF Documents to Teach Undergraduate Graph Theory
 
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Matthew Fairtlough To learn more about the Wolfram Technologies, visit http://www.wolfram.com The European Wolfram Technology Conference featured both introductory and expert sessions on all major technologies and many applications made possible with Wolfram technology. Learn to achieve insightful answers and make better decisions by injecting computation at every level in your data workflows. From capture to analysis to presentation or deployment, topics include the latest in data analysis, instant applications, hybrid cloud-desktop computation and deployment, and multi-paradigm data interfaces and unexpected computation ideas for your data.
Просмотров: 178 Wolfram
Lec-23 Minimum Cost Flow Problem
 
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Lecture series on Advanced Operations Research by Prof. G.Srinivasan, Department of Management Studies, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Просмотров: 81770 nptelhrd
ISRO 2018 | Paper Analysis | Electronics & Communication (EC)
 
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GATE ACADEMY is the India"s leading institute with best teaching practices and most affordable fee. Doubt solving facebook group - https://www.facebook.com/groups/gateconcepts Check out our web & facebook page for more details. 1. Web: www.gateacademy.co.in 2. Email: info@gateacademy.co.in 3. For any doubt join us :https://www.facebook.com/groups/gateconcepts 4. Like us: https://www.facebook.com/gateacademyofficial 5. Instagram - gate_academybhilai Do not Forget to watch the videos of below mentioned subjects : 1. Analog Electronics - https://goo.gl/UFxDKV 2. Engineering Mathematics - https://goo.gl/9rLYLq 3. Signal & System - https://goo.gl/rDfWjC 4. Communication System - https://goo.gl/rDfWjC 5. Analog Electronics | Live Classroom - https://goo.gl/gZUDdK 6. Electrical & Electronic Measurement - https://goo.gl/uZJhES 7. Control Systems - https://goo.gl/fsuwGV 8. Electromagnetic Theory (EMT) - https://goo.gl/ToVDgU 9. Digital Electronics - https://goo.gl/TUR8Fj 10.Network Theory - https://goo.gl/bcNKLB 11.Unfiltered Conversation - https://goo.gl/XziCfi
Просмотров: 15080 GATE ACADEMY
Social Network Theory
 
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Social Network Theory http://depositfiles.com/files/nu53g4kcn
Просмотров: 963 mis4478
Music And Measure Theory
 
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A connection between a classical puzzle about rational numbers and what makes music harmonious.
Просмотров: 467385 3Blue1Brown
Poker Analytics using a Graph Database
 
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Rimma Shafikova discusses efficient collusion detection at VGW
Просмотров: 640 Mechanical Rock Pty Ltd
Overview: Graph Models
 
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Michael Kearns, University of Pennsylvania Unifying Theory and Experiment for Large-Scale Networks http://simons.berkeley.edu/talks/overview-graph-models
Просмотров: 451 Simons Institute
Dynamic Networks and Cyber-Security
 
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As an under-studied area of academic research, the analysis of computer network traffic data is still in its infancy. However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue. This collection of papers by leading researchers and practitioners synthesises cutting-edge work in the analysis of dynamic networks and statistical aspects of cyber security. The book is structured in such a way as to keep security application at the forefront of discussions. It offers readers easy access into the area of data analysis for complex cyber-security applications, with a particular focus on temporal and network aspects. Chapters can be read as standalone sections and provide rich reviews of the latest research within the field of cyber-security. Academic readers will benefit from state-of-the-art descriptions of new methodologies and their extension to real practical problems while industry professionals will appreciate access to more advanced methodology than ever before. Contents: Network Attacks and the Data They Affect (M Morgan, J Sexton, J Neil, A Ricciardi & J Theimer) Cyber-Security Data Sources for Dynamic Network Research (A D Kent) Modelling User Behaviour in a Network Using Computer Event Logs (M J M Turcotte, N A Heard & A D Kent) Network Services as Risk Factors: A Genetic Epidemiology Approach to Cyber-Security (S Gil) Community Detection and Role Identification in Directed Networks: Understanding the Twitter Network of the Care.Data Debate (B Amor, S Vuik, R Callahan, A Darzi, S N Yaliraki & M Barahona) Anomaly Detection for Cyber Security Applications (P Rubin-Delanchy, D J Lawson & N A Heard) Exponential Random Graph Modelling of Static and Dynamic Social Networks (A Caimo) Hierarchical Dynamic Walks (A V Mantzaris, P Grindrod & D J Higham) Temporal Reachability in Dynamic Networks (A Hagberg, N Lemons & S Misra)
Просмотров: 164 World Scientific
What is MATHEMATICAL SOCIOLOGY? What does MATHEMATICAL SOCIOLOGY mean?
 
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What is MATHEMATICAL SOCIOLOGY? What does MATHEMATICAL SOCIOLOGY mean? MATHEMATICAL SOCIOLOGY meaning - MATHEMATICAL SOCIOLOGY definition - MATHEMATICAL SOCIOLOGY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Mathematical sociology is the use of mathematics to construct social theories. Mathematical sociology aims to take sociological theory, which is strong in intuitive content but weak from a formal point of view, and to express it in formal terms. The benefits of this approach include increased clarity and the ability to use mathematics to derive implications of a theory that cannot be arrived at intuitively. In mathematical sociology, the preferred style is encapsulated in the phrase "constructing a mathematical model." This means making specified assumptions about some social phenomenon, expressing them in formal mathematics, and providing an empirical interpretation for the ideas. It also means deducing properties of the model and comparing these with relevant empirical data. Social network analysis is the best-known contribution of this subfield to sociology as a whole and to the scientific community at large. The models typically used in mathematical sociology allow sociologists to understand how predictable local interactions are and they are often able to elicit global patterns of social structure. Starting in the early 1940s, Nicolas Rashevsky, and subsequently in the late 1940s, Anatol Rapoport and others, developed a relational and probabilistic approach to the characterization of large social networks in which the nodes are persons and the links are acquaintanceship. During the late 1940s, formulas were derived that connected local parameters such as closure of contacts – if A is linked to both B and C, then there is a greater than chance probability that B and C are linked to each other – to the global network property of connectivity. Moreover, acquaintanceship is a positive tie, but what about negative ties such as animosity among persons? To tackle this problem, graph theory, which is the mathematical study of abstract representations of networks of points and lines, can be extended to include these two types of links and thereby to create models that represent both positive and negative sentiment relations, which are represented as signed graphs. A signed graph is called balanced if the product of the signs of all relations in every cycle (links in every graph cycle) is positive. This effort led to Harary's Structure Theorem (1953), which says that if a network of interrelated positive and negative ties is balanced, e.g. as illustrated by the psychological principle that "my friend's enemy is my enemy", then it consists of two subnetworks such that each has positive ties among its nodes and there are only negative ties between nodes in distinct subnetworks. The imagery here is of a social system that splits into two cliques. There is, however, a special case where one of the two subnetworks is empty, which might occur in very small networks. In another model, ties have relative strengths. 'Acquaintanceship' can be viewed as a 'weak' tie and 'friendship' is represented as a strong tie. Like its uniform cousin discussed above, there is a concept of closure, called strong triadic closure. A graph satisfies strong triadic closure If A is strongly connected to B, and B is strongly connected to C, then A and C must have a tie (either weak or strong). In these two developments we have mathematical models bearing upon the analysis of structure. Other early influential developments in mathematical sociology pertained to process. For instance, in 1952 Herbert A. Simon produced a mathematical formalization of a published theory of social groups by constructing a model consisting of a deterministic system of differential equations. A formal study of the system led to theorems about the dynamics and the implied equilibrium states of any group.
Просмотров: 568 The Audiopedia
Stability and Robustness of Dynamical Transportation Networks
 
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Ketan Savla is an assistant professor in the Sonny Astani Department of Civil and Environmental Engineering at the University of Southern California. Before joining USC, he was a research scientist in the Laboratory for Information and Decision Systems at MIT. He received his Ph.D. from the University of California, Santa Barbara. His current research interests are in distributed control and estimation of dynamical networks, state-dependent queuing systems and distributed control of mobile agents. The growth in travel demand is set to exceed rate of capacity expansion of our transportation infrastructure. Therefore, a majority of our transportation system is expected to operate at or near its capacity in the near future, and hence more vulnerable to disruptions. This, in combination with higher penetration of smart technology, is expected to induce severe dynamics from users and transportation controllers at multiple scales. It is therefore imperative to develop methodologies to model and control dynamical effects for real-time as well as long-term planning purposes. In this talk, we present our results on stability and robustness properties of dynamical urban transportation networks. The dynamical models consist of mass conservation on the links and flow conservation on the nodes. The routing of flow at the nodes is a result of a combination of driver route choice behavior and real-time control such as adaptive traffic signal control. We present a novel class of routing principles that use only local information for their implementation, and yet can be shown to be maximally stabilizing in presence of minor temporary disturbances and maximally robust towards severe and persistent disruptions. We particularly highlight the role of cascading effects in our analysis. Finally, we propose simple metrics for robustness of transportation networks in terms of network structure, travel demand, route choice and capacity, which are relevant for planning purposes.
Просмотров: 1580 USC Price
Set Theory (Part-5) | Computer Science Engineering
 
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Subject- DMGT Topic - Set Theory Faculty- Mr. G.N. Sir GATE ACADEMY is the India"s leading institute with best teaching practices and most affordable fee. Dear viewer, if you have any doubt on this topic join our interactive session at our FB group to have a direct interaction with our faculties & clarify your doubts. Doubt solving facebook group - https://www.facebook.com/groups/gateconcepts Check out our web & facebook page for more details. 1. Web: www.gateacademy.co.in 2. Email: info@gateacademy.co.in 3. For any doubt join us :https://www.facebook.com/groups/gateconcepts 4. Like us: https://www.facebook.com/gateacademyofficial 5. Instagram - gate_academybhilai Do not Forget to watch the videos of below mentioned subjects : 1. Analog Electronics - https://goo.gl/UFxDKV 2. Engineering Mathematics - https://goo.gl/9rLYLq 3. Signal & System - https://goo.gl/rDfWjC 4. Communication System - https://goo.gl/rDfWjC 5. Analog Electronics | Live Classroom - https://goo.gl/gZUDdK 6. Electrical & Electronic Measurement - https://goo.gl/uZJhES 7. Control Systems - https://goo.gl/fsuwGV 8. Electromagnetic Theory (EMT) - https://goo.gl/ToVDgU 9. Digital Electronics - https://goo.gl/TUR8Fj 10.Network Theory - https://goo.gl/bcNKLB 11.Unfiltered Conversation - https://goo.gl/XziCfi
Просмотров: 729 GATE ACADEMY
Graph Theory: Number of Routes and Circuits of a Complete Graph
 
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This lesson explains how to find the total number of routes and circuits of complete graphs. Site: http://mathispower4u.com
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9   3   Random Contraction Algorithm 9 min
 
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Просмотров: 4179 Stanford Algorithms
4. Control System- Preparation Strategy for GATE 2018/19 (EC)
 
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This is a preparation strategy for GATE 2018/19 specially for those who are appearing in exam for Electronics Engineering. This video explains the various do's and dont's a student must follow while preparing for his/her GATE attempt. Mr. Umesh Dhande explains subject wise approach to every subject and thus provides best guidelines to students. In this video he explains the important topics and strategy for subject "CONTROL SYSTEM". 1. Network Theory https://www.youtube.com/watch?v=UAgO7GW5Frs 2. Signal and System https://www.youtube.com/watch?v=KH3J6jGEUPg 3. Digital Electronics and 8085 https://www.youtube.com/watch?v=TutFY0P23pY 4. Control System https://www.youtube.com/watch?v=NiDNiwWbmwA 5. Electronic Devices https://www.youtube.com/watch?v=fj1Opkp02h8 6. Analog Electronics https://www.youtube.com/watch?v=bpIYfwJZceI 7. Electromagnetic Theory https://www.youtube.com/watch?v=Kb6pdgTRPEo 8. Communication System https://www.youtube.com/watch?v=OMhT_RbEazc 9. Engineering Mathematics and Aptitude https://www.youtube.com/watch?v=1hDEPbvyCMw For joining closed facebook group- https://www.facebook.com/groups/gateconcepts/ GATE Academy facebook page- https://www.facebook.com/gateacademy.official/ Online test series for gate- onlinetestseries.gateacademy.co.in For more such videos, lectures and tutorials subscribe to our youtube channel.
Просмотров: 23332 GATE ACADEMY
Uniform Circular Motion: Crash Course Physics #7
 
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Did you know that centrifugal force isn't really a thing? I mean, it's a thing, it's just not real. In fact, physicists call it a "Fictitious Force." Mind blown yet? To explore this idea further, this week Shini sits down with us to discuss centripetal force, centrifugal force, and a few other bits of physics to help us understand Uniform Circular motion. *** Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Help PBSDS win a Webby Award by voting here: https://pv.webbyawards.com/2016/online-film-video/video-channels-and-networks/science-education Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark, Eric Kitchen, Jessica Wode, Jeffrey Thompson, Steve Marshall, Moritz Schmidt, Robert Kunz, Tim Curwick, Jason A Saslow, SR Foxley, Elliot Beter, Jacob Ash, Christian, Jan Schmid, Jirat, Christy Huddleston, Daniel Baulig, Chris Peters, Anna-Ester Volozh, Ian Dundore, Caleb Weeks -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashC... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support CrashCourse on Patreon: http://www.patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Просмотров: 722029 CrashCourse
network modelling using game theory
 
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Просмотров: 335 Yuvi Raj