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Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Inf.Theory and Entropy

Jakob Foerster 97 views

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clipping reduces as it goes on, information content regained, i think maybe you shouts when you are nervous more interstitial melodies, perhaps the beautiful entropy
Comment from : @eddiehazel1259

thanks for uploading these excellent lectures (RIP David
Comment from : @frasio3930

Sigh totally lost back to the beginning of the video
Comment from : @iirclife

Rest in peace David :) Thanks for the lectures
Comment from : @giantbee9763

Sir D Mackay was an inspiration RIP
Comment from : @LucasSilva-rf5mf

At 46:00 of this excellent video, Professor MacKay agrees with the bbrilliant/b student question that maximizing entropy (or asking questions which maximize equiprobable outcomes) is bnot/b always the best strategy in these games brbrWhy not? Scratching my head in Silicon Valley! Matt
Comment from : @Crashof2008

RIP
Comment from : @afarro

Repetition (redundancy) is dual to variation -- musicbrCertainty is dual to uncertainty -- the Heisenberg certainty/uncertainty principlebrSyntropy (prediction) is dual to increasing entropy -- the 4th law of thermodynamicsbrRandomness (entropy) is dual to order (predictability) -- "Always two there are" -- YodabrTeleological physics (syntropy) is dual to non teleological physicsbrDuality: two sides of the same coin
Comment from : @hyperduality2838

Fantastic course ! Does it exist in 720p by any chance ?
Comment from : @-gbogbo-

Revision: Upto 2:30brbr5:05 In standard communication system what we want to do is exploit redundancy that already exists in the real source and get rid of it, shrink the file (ie compress it first to make it idealize source) so we can put into channel coding method brbrThe Bent Coin 7:15brShannon's Claim: 11:00brEntropy: 19:30brThe Weighing Problem Solution: 21:08brThe Bent Coin Lottery: 47:15
Comment from : @prajwolgyawali6770

The question as stated is about minimizing the guaranteed number of weighings, not the expected number of weighings, so we really ought to be maximizing the minimum information we can get from each weighing, rather than the entropy
Comment from : @isupeene

In case anybody is interested I wrote a demo for the weighing problem in matlab githubcom/siaahmadi/weighingproblem
Comment from : @siaahmadi413

high-value talk, thank you for uploading
Comment from : @mariannarespond21

at 34:12, why is the probability 2/8 in even case of HHL,HHL ? and in case of GGG,LLL (LHHHH), why are the probability 0, 5/8, 3/8??
Comment from : @itwipsy17

Interesting lecture but I don't understand the second weighing entropy computation How can 4 good balls and 4 light balls result in balance weighing with probability 05?brBeside that, the odd ball is only one and either lighter or heavier How can heavy and light ball assumed appear at the same time?brbrAny help will be very appreciated Thank you very much
Comment from : @kuntoroan

I find this fascinating and was trying to work out the math How did he get 148 bits on the 5x5 example at 26:22? I tried averaging log base 2 of 5/12,2/12, and 5/12 divided by 3 but that is clearly wrong Would appreciate any help Thanks
Comment from : @stephenwaite4737

Excellent,Long live
Comment from : @abusesay2052

I sense a major conflict between Shannon's model and economic creativity theories of GIlder Reducing redundancy, noise reduction and lossless transmissions are contraindicative to Gilder's idea of creativity and growth Any responses on this?
Comment from : @Nahulanham