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Definition and basic properties of information entropy (a.k.a. Shannon entropy)
Views: 86173 mathematicalmonk

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Entropy is a measure of the uncertainty in a random variable (message source). Claude Shannon defines the "bit" as the unit of entropy (which is the uncertainty of a fair coin flip). In this video information entropy is introduced intuitively using bounce machines & yes/no questions. Note: This analogy applies to higher order approximations, we simply create a machine for each state and average over all machines!
Views: 105443 Art of the Problem

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This video will talk discuss random number generation and how it relates to encryption in computers. This will look at why random numbers are important, how computers secure generate numbers as well as some challenges and new technologies to overcome these.
Views: 421 Daniel Lohin

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What is the essence of information? We explore the history of communication technology leading to the modern field of information theory. We'll build up towards Claude Shannon's measure of information entropy, one step at a time.
Views: 194633 Art of the Problem

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Views: 429 Chuck Moore

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To illustrate the randomness of entropy, 2 dice are rolled to show the random vs. non-random states. More Physical chemistry tutorials on http://www.mchmultimedia.com. Also see my blog http://quantummechanics.mchmultimedia.com
Views: 3045 bryansanctuary

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Claude Shannon - colossus of Information Theory. Entropy. The term was coined to describe a quantity that arose in the laws of Thermodynamics, and is often summarised as being a measure of disorder. One of Shannon's great insights was to see that this term could be used to describe the unpredictability of signals. Information. This term was chosen by Shannon to describe what is sent by signalling a message from one location to another. He defines it strictly and he does not include meaning in the definition, but rather the density of information is judged by it's lack of redundancy, its unpredictability. Randomness. Another concept which sounds deceptively simple, but which requires careful definition for mathematicians to tap into its power. A number is random if it is not possible to write an instruction to produce it that is shorter than writing out the number itself in full. Thus a number such as 11111111111111111 is not random because you can say "17 ones" and capture it. But also a number that looks random such as the decimal places of pi, may not be. There is a mathematical recipe for pi that you can tell someone to work out to get pi to any desired level of accuracy. As yet no one has detected any pattern to the decimal places of the number. The recipe is "Take 4, subtract 4/3, add 4/5, subtract 4/7 etc etc" it gets closer and closer to pi as you keep adding and subtracting smaller bits. Complexity. Similar to randomness, but where they can differ in certain contexts is in recognising "logical depth" Charles Bennett defined logical depth thus "What might be called its buried redundancy - parts predictable only with difficulty, things the receiver could in principle have figured out without being told, but only at considerable cost in money, time, or computation." The term can be used to convey the quality that holds the greatest amount of meaning for us in a message. Noise. Noise is not just the result of faulty design or construction of communication equipment, it is a fundamental property of electrical components. The equations that describe it were derived by Albert Einstein to describe Brownian motion - the movement of microscopic pollen in water that had been a puzzle until Einstein published his paper in 1905 (the same year he published the 'special relativity' and the 'electrostatic effect' papers - quite a year) Shannon's noisy coding theorem shows that error correction can effectively counter noise and corruption, at the cost of redundancy and extra computation.
Views: 625 John Harmer

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Watch this video and understand what actually disorderness is.
Views: 63 Pushpkar Karn

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A brief introduction of approximate entropy. Correction: I misspelled Renyi as Renmi and he was Hungarian instead of French.
Views: 2107 ousam2010

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What is Information? - Part 2a - Introduction to Information Theory: Script: http://crackingthenutshell.org/what-is-information-part-2a-information-theory ** Please support my channel by becoming a patron: http://www.patreon.com/crackingthenutshell ** Or... how about a Paypal Donation? http://crackingthenutshell.org/donate Thanks so much for your support! :-) - Claude Shannon - Bell Labs - Father of Information Theory - A Mathematical Theory of Communication - 1948 - Book, co-written with Warren Weaver - How to transmit information efficiently, reliably & securely through a given channel (e.g. tackling evesdropping) - Applications. Lossless data compression (ZIP files). Lossy data compression (MP3, JPG). Cryptography, thermal physics, quantum computing, neurobiology - Shannon's definition not related to meaningfulness, value or other qualitative properties - theory tackles practical issues - Shannon's information, a purely quantitative measure of communication exchanges - Shannon's Entropy. John von Neumann. Shannon's information, information entropy - avoid confusion with with thermodynamical entropy - Shannon's Entropy formula. H as the negative of a certain sum involving probabilities - Examples: fair coin & two-headed coin - Information gain = uncertainty reduction in the receiver's knowledge - Shannon's entropy as missing information, lack of information - Estimating the entropy per character of the written English language - Constraints such as "I before E except after C" reduce H per symbol - Taking into account redundancy & contextuality - Redundancy, predictability, entropy per character, compressibility - What is data compression? - Extracting redundancy - Source Coding Theorem. Entropy as a lower limit for lossless data compression. - ASCII codes - Example using Huffman code. David Huffman. Variable length coding - Other compression techniques: arithmetic coding - Quality vs Quantity of information - John Tukey's bit vs Shannon's bit - Difference between storage bit & information content. Encoded data vs Shannon's information - Coming in the next video: error correction and detection, Noisy-channel coding theorem, error-correcting codes, Hamming codes, James Gates discovery, the laws of physics, How does Nature store Information, biology, DNA, cosmological & biological evolution
Views: 56598 Cracking The Nutshell

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Passwords, introduction to password entropy. Lecture 25 of CSS322 Security and Cryptography at Sirindhorn International Institute of Technology, Thammasat University. Given on 27 February 2014 at Bangkadi, Pathumthani, Thailand by Steven Gordon. Course material via: http://sandilands.info/sgordon/teaching
Views: 1867 Steven Gordon

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Views: 105 MDPI Sciforum

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Video shows what entropy means. strictly thermodynamic entropy. A measure of the amount of energy in a physical system that cannot be used to do work. A measure of the disorder present in a system. The capacity factor for thermal energy that is hidden with respect to temperature [http://arxiv.org/pdf/physics/0004055]. The dispersal of energy; how much energy is spread out in a process, or how widely spread out it becomes, at a specific temperature. [http://www.entropysite.com/students_approach.html]. A measure of the amount of information and noise present in a signal. Originally a tongue-in-cheek coinage, has fallen into disuse to avoid confusion with thermodynamic entropy.. The tendency of a system that is left to itself to descend into chaos.. entropy pronunciation. How to pronounce, definition by Wiktionary dictionary. entropy meaning. Powered by MaryTTS
Views: 4927 SDictionary

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Math 574, Topics in Logic Penn State, Spring 2014 Instructor: Jan Reimann
Views: 739 Jan Reimann

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Video shows what algorithmic entropy means. Kolmogorov complexity. Algorithmic entropy Meaning. How to pronounce, definition audio dictionary. How to say algorithmic entropy. Powered by MaryTTS, Wiktionary

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Views: 51823 Khan Academy Labs

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Intuition-building examples for information entropy
Views: 23571 mathematicalmonk

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In this video Kais Abdelhkalek presents a review and overview of the recent paper: "Entropic uncertainty and measurement reversibility" by Mario Berta, Stephanie Wehner, Mark M. Wilde, arXiv:1511.00267
Views: 203 QIG Hannover

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Views: 61489 Khan Academy Labs

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- surveillance - choke point - need to know - don't do crypto yourself Cryptographic primitives - hash functions and their basic properties - pseudo-random number generators - determinism - period - entropy - /dev/random vs /dev/urandom
Views: 277 ralienpp

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Himanshu Tyagi, Indian Institute of Science Information Theory, Learning and Big Data http://simons.berkeley.edu/talks/himanshu-tyagi-2015-03-17
Views: 2165 Simons Institute

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Views: 1117 IST OnlineLearning

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Unit 4: Probability, Lecture 2 Instructors: Paul Penfield, Seth Lloyd See the complete course at: http://ocw.mit.edu/6-050js08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 15970 MIT OpenCourseWare

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Views: 45353 Khan Academy Labs

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Views: 7923 ousam2010

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Like the video and Subscribe to channel for more updates. Recommended Books (5 Books , Please buy anything from the below links to support the channel): A Students Guide to Coding and Information Theory http://amzn.to/2zo0MN8 Information Theory, Coding & Cryptography, 1e http://amzn.to/2D72DrX Information Theory and Coding http://amzn.to/2C3n1gv Information Theory, Coding and Cryptography http://amzn.to/2zpRCPW Information Theory and Coding: Basics and Practices http://amzn.to/2zpJQFV
Views: 30 KNOWLEDGE TREE

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Views: 734 iqra tarar

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Views: 51337 Khan Academy Labs

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Views: 132425 Khan Academy Labs

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Track 15 of 21 from the compilation "Everyday is Halloween II". Released on CDr on the label Nursing Home and limited to 150.

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#breakthroughjuniorchallenge What keeps atoms from flying apart? Find out now! 2017 Breakthrough Junior Challenge submission by Heather Lanphear
Views: 202 Heather Lanphear

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Views: 10 wikipedia tts

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Today the Search Engine Google is showing an animated Doodle for celebrating Claude Shannon’s 100th birthday. Claude Shannon was an American mathematician, electrical engineer, and cryptographer known as "the father of information theory". Shannon is famous for having founded information theory with a landmark paper that he published in 1948. He is perhaps equally well known for founding both digital computer and digital circuit design theory in 1937, when, as a 21-year-old master's degree student at the Massachusetts Institute of Technology (MIT), he wrote his thesis demonstrating that electrical applications of Boolean algebra could construct any logical, numerical relationship. Read more details about Claude Shannon at https://en.wikipedia.org/wiki/Claude_Shannon

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Lecture by Vladimir Nesterov. Full member of Academy of Medical and Technical Sciences, President of International Academy of non-linear diagnostic systems
Views: 1347 Metatron IPP

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Views: 50 Jose Ortega

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Learning representations is arguably the central problem in machine learning, and symmetry group theory is a natural foundation for it. A symmetry of a classifier is a representation change that doesn't change the examples' classes. The goal of representation learning is to get rid of unimportant variations, making important ones easy to detect, and unimportant variations are symmetries of the target function. Exploiting symmetries reduces sample complexity, leads to new generalizations of classic learning algorithms, provides a new approach to deep learning, and is applicable with all types of machine learning. In this talk I will present three approaches to symmetry-based learning: (1) Exchangeable variable models are distributions that are invariant under permutations of subsets of the variables. They subsume existing tractable independence-based models and difficult cases like parity functions, and outperform SVMs and state-of-the-art probabilistic classifiers. (2) Deep symmetry networks generalize convolutional neural networks by tying parameters and pooling over an arbitrary symmetry group, not just the translation group. In preliminary experiments, they outperformed convnets on a digit recognition task. (3) Symmetry-based semantic parsing defines a symmetry of a sentence as a syntactic transformation that preserves its meaning. The meaning of a sentence is thus its orbit under the semantic symmetry group of the language. This allows us to map sentences to their meanings without pre-defining a formal meaning representation or requiring labeled data in the form of sentence-formal meaning pairs, and achieved promising results in a paraphrase detection problem. (Joint work with Rob Gens, Chloe Kiddon and Mathias Niepert.)
Views: 859 Microsoft Research

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MIT 6.S095 Programming for the Puzzled, IAP 2018 View the complete course: https://ocw.mit.edu/6-S095IAP18 Instructor: Srini Devadas Given a set of identical balls, you need to discover how high you can climb and drop a ball without the ball breaking. This video describes the algorithm and associated program to solve this problem while minimizing the number of required drops. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 4170 MIT OpenCourseWare

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IBM Fellow Charles Bennett on how weird physical phenomena discovered in the early 20th century have taught us the true nature of information, and how to process it.
Views: 14626 IBM Research

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Views: 46482 Khan Academy Labs

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Views: 3295576 VICE

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Views: 71 GTPHYS MOOC

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Views: 66599 Khan Academy Labs

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For accessing 7Activestudio videos on mobile Download SCIENCETUTS App to Access 120+ hours of Free digital content. For more information: http://www.7activestudio.com [email protected] http://www.7activemedical.com/ [email protected] http://www.sciencetuts.com/ Contact: +91- 9700061777, 040-64501777 / 65864777 7 Active Technology Solutions Pvt.Ltd. is an educational 3D digital content provider for K-12. We also customise the content as per your requirement for companies platform providers colleges etc . 7 Active driving force "The Joy of Happy Learning" -- is what makes difference from other digital content providers. We consider Student needs, Lecturer needs and College needs in designing the 3D & 2D Animated Video Lectures. We are carrying a huge 3D Digital Library ready to use. The Pauli exclusion principle is the quantum mechanical principle that states that two or more identical fermions (particles with half-integer spin) cannot occupy the same quantum state within a quantum system simultaneously.
Views: 28663 7activestudio

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