Definition and basic properties of information entropy (a.k.a. Shannon entropy)

Views: 86173
mathematicalmonk

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

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

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

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

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

Watch this video and understand what actually disorderness is.

Views: 63
Pushpkar Karn

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

Views: 2107
ousam2010

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

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

Views: 105
MDPI Sciforum

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

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

Views: 739
Jan Reimann

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

Views: 58
ADictionary

How can we quantify/measure an information source?
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/markov_chains?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/intro-to-channel-capacity-information-theory?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 51823
Khan Academy Labs

Intuition-building examples for information entropy

Views: 23571
mathematicalmonk

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

Introduction to Symbol Rate (Baud)
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/intro-to-channel-capacity-information-theory?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/info-theory/v/morse-code-the-information-age-language-of-coins-8-12?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 61489
Khan Academy Labs

- 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

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

Views: 1117
IST OnlineLearning

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

Introduction to coding theory!
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/info-theory/v/history-of-optical-telegraphs-language-of-coins-5-9?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/info-theory/v/rosetta-stone-196-b-c-e?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 45353
Khan Academy Labs

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

Introduction to Channel Capacity & Message Space
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/how-do-we-measure-information-language-of-coins-10-12?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/symbol-rate-information-theory?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 51337
Khan Academy Labs

WW2 Encryption is explored with a focus on the Enigma. Read more here.
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/perfect-secrecy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/frequency-stability?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 189719
Khan Academy Labs

Claude Shannon's idea of perfect secrecy: no amount of computational power can help improve your ability to break the one-time pad
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/random-vs-pseudorandom-number-generators?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/case-study-ww2-encryption-machines?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 132425
Khan Academy Labs

موجات صوتيه مع فيديوجاهزه للمونتاج
انترو مجانية
عمل انترو مجاني
موقع انترو مجاني
تحميل انترو مجاني
انترو احترافي مجاني
انترو فلاش مجانا
صنع انترو مجاني
تصميم انترو مجاني
انترو فيديو مجاني
موقع عمل انترو مجاني
انترو مجاني
entropy
entropy change
entropi nedir
entropy journal
entropy matlab
entropy calculator
entropy medikal
entropy and enthalpy
entropy formulas
entropy coding
entropy formula
entropy meaning
entropy and information
entropy and life
entropy and thermodynamics
entropy and example
entropy and boltzmann
entropy and specific entropy
entropy and units
entropy and mixing
entropy and bsi
a level entropy
a positive entropy means
a maximum entropy approach to natural language processing
a negative entropy means
a system's entropy is
a negative entropy change indicates
a positive entropy
a negative entropy change
a positive entropy change
entropy balance equation
entropy balance
entropy by thomas pynchon
entropy book
entropy by thomas pynchon summary
entropy boltzmann
entropy biology
entropy bsi
entropy black hole
entropy band
b entropy
ap physics b entropy
binary entropy
entropy change in adiabatic process
entropy calculation
entropy chemistry
entropy change in irreversible process
entropy calculation example
entropy change equation
entropy cryptography
c entropy
ent c entropy
c program for entropy
entropy by k c cole
entropy o.a.c
shannon entropy c
shannon entropy c code
entropy of c graphite
entropy coding in c
c&c maximum entropy tagger
entropy definition
entropy decision tree
entropy data mining
entropy definition thermodynamics
entropy dragonfable
entropy derivation
entropy disorder
entropy distribution
entropy definition chemistry
entropy def
d&d entropy
d&d entropy magic
init.d entropy
d&d 3.5 entropy domain
d d ray of entropy
d&d god of entropy
d&d miasma of entropy
entropy domain d&d
entropy script init.d
d ther high entropy
entropy equation
entropy examples
entropy equation for a control volume
entropy example problems
entropy energy
entropy economics
entropy estimation
entropy equations
entropy explained
e entropy
e-entropy ge
e-entropy-00
e entropy ge healthcare
e entropy monitor
datex ohmeda e-entropy
datex e-entropy
entropy e.u
e cell and entropy
okuma entropy+e
entropy function
entropi formülü
entropy film
entropy fixer
entropy for dummies
entropy for ideal gas
entropy for vaporization
entropy formation
f entropy
entropy h(f(x))
f*head entropy
entropy f-measure
entropy generation minimization
entropy generation formula
entropy generation
entropy game
entropy grimes
entropy gameplay
entropy gw2
entropy generation through heat and fluid flow
entropy grimes lyrics
entropy gravity
g entropy
g entropy enthalpy
h2o(g) entropy
n2h4(g) entropy
ch3cho(g) entropy
ch3oh(g) entropy
delta g entropy enthalpy
delta s entropy
entropy of hcl (g)
entropy of co2 g
entropy heat
entropy happens
entropy hillcrest
entropy hours
entropy hindi
entropy heat capacity
entropy h
entropy hyperphysics
entropy happens shirt
entropy heat death
h entropy
delta h entropy
positive delta h entropy
h theorem entropy
h.264 entropy
entropy of h+ ion
entropy of h+(aq)
entropy coding in h.264 avc
entropy h arduino
entropy indir
entropy is a monotonically increasing function
entropi ilkesi
entropy information theory
entropy image processing
entropy in thermodynamics
entropy in statistics
entropy index
entropy in matlab
entropy isn't what it used to be
entropy jokes
entropy journal reputation
entropy jewelry
entropy journal glyphosate
entropy jabong
entropy java
entropy jokes one liner
entropy jagex
entropy just isn't what it used to be
j willard gibbs entropy
entropy j mol k
entropy image j
entropy j/k
entropy là j
entropy khan academy
entropy kurtis
entropy kurtas
entropy khan
entropy kill bill
entropy kc cole
entropy king of spain
entropy kyubey
entropy kullback leibler
entropy knowledge network
k entropy
entropy s = k lnw
entropy weighted k means
entropy k log w
entropy at 298 k
entropy at 0k
k means entropy
k constant for entropy
entropy based soft k means clustering
entropy law
entropy lecture notes
entropy lol
entropy lyrics
entropy label
entropy lecture
entropy less than 0
entropy logarithm
entropy loss function
entropy law of thermodynamics
l entropy
hcooh(l) entropy
ch3ch2oh(l) entropy
man-l entropy
entropy spain s.l
entropy l diversity
entropy of c2h5oh(l)
entropy determinants and $l^ 2 $-torsion
maximum entropy l bfgs
entropy zero s l
entropy machine learning
entropy mac
entropy mmo
entropy maximization
entropy means
entropy matlab code
entropy movie
m-entropy module
m-entropy
m scott peck entropy
entropyfilt.m
entropy.m matlab
entropy m-w
entropy 2.m
octave entropy.m
entropy_fun.m
approximate entropy m

Views: 9556
Digital Droid ديجتل درويد

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

Views: 48
RareNoiseUploads

Start your FREE Trial of The Great Courses Plus and watch the course here: https://www.thegreatcoursesplus.com/special-offer?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=145596
The science of information is the most influential, yet perhaps least appreciated field in science today. Never before in history have we been able to acquire, record, communicate, and use information in so many different forms. Never before have we had access to such vast quantities of data of every kind. This revolution goes far beyond the limitless content that fills our lives, because information also underlies our understanding of ourselves, the natural world, and the universe. It is the key that unites fields as different as linguistics, cryptography, neuroscience, genetics, economics, and quantum mechanics. And the fact that information bears no necessary connection to meaning makes it a profound puzzle that people with a passion for philosophy have pondered for centuries.
Little wonder that an entirely new science has arisen that is devoted to deepening our understanding of information and our ability to use it. Called information theory, this field has been responsible for path-breaking insights such as the following:
What is information? In 1948, mathematician Claude Shannon boldly captured the essence of information with a definition that doesn’t invoke abstract concepts such as meaning or knowledge. In Shannon’s revolutionary view, information is simply the ability to distinguish reliably among possible alternatives.
The bit: Atomic theory has the atom. Information theory has the bit: the basic unit of information. Proposed by Shannon’s colleague at Bell Labs, John Tukey, bit stands for “binary digit”—0 or 1 in binary notation, which can be implemented with a simple on/off switch. Everything from books to black holes can be measured in bits.
Redundancy: Redundancy in information may seem like mere inefficiency, but it is a crucial feature of information of all types, including languages and DNA, since it provides built-in error correction for mistakes and noise. Redundancy is also the key to breaking secret codes.
Building on these and other fundamental principles, information theory spawned the digital revolution of today, just as the discoveries of Galileo and Newton laid the foundation for the scientific revolution four centuries ago. Technologies for computing, telecommunication, and encryption are now common, and it’s easy to forget that these powerful technologies and techniques had their own Galileos and Newtons.
The Science of Information: From Language to Black Holes covers the exciting concepts, history, and applications of information theory in 24 challenging and eye-opening half-hour lectures taught by Professor Benjamin Schumacher of Kenyon College. A prominent physicist and award-winning educator at one of the nation’s top liberal arts colleges, Professor Schumacher is also a pioneer in the field of quantum information, which is the latest exciting development in this dynamic scientific field.
Start your FREE Trial of The Great Courses Plus and watch the course here: https://www.thegreatcoursesplus.com/special-offer?utm_source=US_OnlineVideo&utm_medium=SocialMediaEditorialYouTube&utm_campaign=145596
Don’t forget to subscribe to our channel – we are adding new videos all the time! https://www.youtube.com/subscription_center?add_user=TheGreatCourses

Views: 2081
The Great Courses Plus

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

Views: 202
Heather Lanphear

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Entropy_(information_theory)
00:03:58 1 Introduction
00:10:25 2 Definition
00:13:01 3 Example
00:13:13 4 Rationale
00:14:50 5 Aspects
00:15:09 5.1 Relationship to thermodynamic entropy
00:17:59 5.2 Entropy as information content
00:18:46 5.3 Entropy as a measure of diversity
00:24:17 5.4 Data compression
00:24:26 5.5 World's technological capacity to store and communicate information
00:29:38 5.6 Limitations of entropy as information content
00:31:32 5.7 Limitations of entropy in cryptography
00:31:55 5.8 Data as a Markov process
00:32:28 5.9 spanib/i
00:33:23 6 Efficiency
00:36:35 7 Characterization
00:36:53 7.1 Continuity
00:37:02 7.2 Symmetry
00:38:46 7.3 Maximum
00:41:28 7.4 Additivity
00:41:46 8 Further properties
00:42:01 9 Extending discrete entropy to the continuous case
00:43:10 9.1 Differential entropy
00:43:52 9.2 Limiting density of discrete points
00:45:57 9.3 Relative entropy
00:46:47 10 Use in combinatorics
00:46:59 10.1 Loomis–Whitney inequality
00:47:03 10.2 Approximation to binomial coefficient
00:47:12 11 See also
00:47:24 12 References
00:48:15 13 Further reading
00:49:45 13.1 Textbooks on information theory
00:52:00 14 External links
00:52:10 bn
00:52:24 0. This implies that the efficiency of a source alphabet with n symbols can be defined simply as being equal to its n-ary entropy. See also Redundancy (information theory).
00:52:39 Further properties
01:00:04 Extending discrete entropy to the continuous case
01:00:16 Differential entropy
01:08:09 Limiting density of discrete points
01:10:14 Relative entropy
01:11:54 m as measures. It is defined for any measure space, hence coordinate independent and invariant under co-ordinate reparameterizations if one properly takes into account the transformation of the measure m. The relative entropy, and implicitly entropy and differential entropy, do depend on the "reference" measure m.
01:12:16 Use in combinatorics
01:12:27 Entropy has become a useful quantity in combinatorics.
01:12:34 Loomis–Whitney inequality
01:15:46 log|A|, where |A| denotes the cardinality of A. Let Si
01:16:55 Approximation to binomial coefficient
Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.
Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain
Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.
Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.9984535686745089
Voice name: en-GB-Wavenet-D
"I cannot teach anybody anything, I can only make them think."
- Socrates
SUMMARY
=======
Information entropy is the average rate at which information is produced by a stochastic source of data.
The measure of information entropy associated with each possible data value is the negative logarithm of the probability mass function for the value:
S
=
−
∑
i
P
i
ln
P
i
S=-\sum _{i}P_{i}\ln {P_{i}}
. When the data source has a lower-probability value (i.e., when a low-probability event occurs), the event carries more "information" ("surprisal") than when the source data has a higher-probability value. The amount of information conveyed by each event defined in this way becomes a random variable whose expected value is the information entropy. Generally, entropy refers to disorder or uncertainty, and the definition of entropy used in information theory is directly analogous to the definition used in statistical thermodynamics. The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication".The basic model of a data communication system is composed of three elements, a source of data, a communication channel, and a receiver, and – as expressed by Shannon – the "fundamental problem of communication" is for the receiver to be able to identify what data was generated by the source, based on the signal it receives through the channel. The entropy provides an absolute limit on the shortest possible average leng ...

Views: 10
wikipedia tts

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

Views: 778
Rajamanickam Antonimuthu

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

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

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

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

What is the limit of compression?
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/testtest?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/information-entropy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 46482
Khan Academy Labs

The global economy is in crisis. The exponential exhaustion of natural resources, declining productivity, slow growth, rising unemployment, and steep inequality, forces us to rethink our economic models. Where do we go from here? In this feature-length documentary, social and economic theorist Jeremy Rifkin lays out a road map to usher in a new economic system.
A Third Industrial Revolution is unfolding with the convergence of three pivotal technologies: an ultra-fast 5G communication internet, a renewable energy internet, and a driverless mobility internet, all connected to the Internet of Things embedded across society and the environment.
This 21st century smart digital infrastructure is giving rise to a radical new sharing economy that is transforming the way we manage, power and move economic life. But with climate change now ravaging the planet, it needs to happen fast. Change of this magnitude requires political will and a profound ideological shift.
To learn more visit: https://impact.vice.com/thethirdindustrialrevolution
Click here to subscribe to VICE: http://bit.ly/Subscribe-to-VICE
Check out our full video catalog: http://bit.ly/VICE-Videos
Videos, daily editorial and more: http://vice.com
More videos from the VICE network: https://www.fb.com/vicevideo
Click here to get the best of VICE daily: http://bit.ly/1SquZ6v
Like VICE on Facebook: http://fb.com/vice
Follow VICE on Twitter: http://twitter.com/vice
Follow us on Instagram: http://instagram.com/vice
Download VICE on iOS: http://apple.co/28Vgmqz
Download VICE on Android: http://bit.ly/28S8Et0

Views: 3295576
VICE

Views: 71
GTPHYS MOOC

Claude Shannon demonstrated how to generate "english looking" text using Markov chains.
Watch the next lesson: https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/information-entropy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/informationtheory/moderninfotheory/v/markov_chains?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience
Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 66599
Khan Academy Labs

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

How can you see into the past? Why can't you burp in Space?
A list of the top 50 physics facts to blow your mind.
Hi! I'm Jade. I make short, simple physics videos that can be understood by everyone.
***SUBSCRIBE***
https://www.youtube.com/c/upandatom
***Let's be friends***
TWITTER: https://twitter.com/upndatom?lang=en
***OTHER VIDEOS YOU'LL LOVE***
A Quantum Paradox: The Exploding Bomb and the Photon https://youtu.be/wiW7jhdKDVA
How Many Dimensions Can You See? https://youtu.be/cwWbSVzAFLQ
MUSIC: Back on Track - Latinesque by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/)
Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100426
Artist: http://incompetech.com/
Thanks for watching!

Views: 24209
Up and Atom

Fabian Furrer of Institut für Theoretische Physik, Universität Hannover and the University of Tokyo presented: Continuous variable entropic uncertainty relations in the presence of quantum memory on behalf of his co-authors Mario Berta (Institut für Theoretische Physik, ETH Zürich), Matthias Christandl (Institut für Theoretische Physik, ETH Zürich), Volkher Schultz (Institut für Theoretische Physik, ETH Zürich) and Marco Tomamichel (Centre for Quantum Technologies, National University of Singapore) at the 2013 QCrypt Conference in August. http://2013.qcrypt.net
Find out more about IQC!
Website - https://uwaterloo.ca/institute-for-quantum-computing/
Facebook - https://www.facebook.com/QuantumIQC
Twitter - https://twitter.com/QuantumIQC

Views: 439
Institute for Quantum Computing

In information theory, entropy is a measure of the uncertainty in a random variable. In this context, the term usually refers to the Shannon entropy, which quantifies the expected value of the information contained in a message. Entropy is typically measured in bits, nats, or bans. Shannon entropy is the average unpredictability in a random variable, which is equivalent to its information content (with the opposite sign). Shannon entropy provides an absolute limit on the best possible lossless encoding or compression of any communication, assuming that the communication may be represented as a sequence of independent and identically distributed random variables.
A single toss of a fair coin has an entropy of one bit. A series of two fair coin tosses has an entropy of two bits. The number of fair coin tosses is its entropy in bits. This random selection between two outcomes in a sequence over time, whether the outcomes are equally probable or not, is often referred to as a Bernoulli process. The entropy of such a process is given by the binary entropy function. The entropy rate for a fair coin toss is one bit per toss. However, if the coin is not fair, then the uncertainty, and hence the entropy rate, is lower. This is because, if asked to predict the next outcome, we could choose the most frequent result and be right more often than wrong. The difference between what we know, or predict, and the information that the unfair coin toss reveals to us is less than one heads-or-tails "message", or bit, per toss.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video

Views: 4556
Audiopedia