| Title | : | Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Inf.Theory and Entropy |
| Lasting | : | 51.09 |
| Date of publication | : | |
| Views | : | 97 rb |
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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 |
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thanks for uploading these excellent lectures (RIP David Comment from : @frasio3930 |
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Sigh totally lost back to the beginning of the video Comment from : @iirclife |
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Rest in peace David :) Thanks for the lectures Comment from : @giantbee9763 |
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Sir D Mackay was an inspiration RIP Comment from : @LucasSilva-rf5mf |
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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 |
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RIP Comment from : @afarro |
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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 |
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Fantastic course ! Does it exist in 720p by any chance ? Comment from : @-gbogbo- |
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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 |
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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 |
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In case anybody is interested I wrote a demo for the weighing problem in matlab githubcom/siaahmadi/weighingproblem Comment from : @siaahmadi413 |
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high-value talk, thank you for uploading Comment from : @mariannarespond21 |
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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 |
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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 |
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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 |
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Excellent,Long live Comment from : @abusesay2052 |
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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 |
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Vielen Dank, Das hat mir sehr geholfen Comment from : @hashemmohammed4420 |
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Noisy channel audio should have been cleaned up first ! It's distorted in fact Comment from : @jackevans2386 |
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Thank you so much for uploading these They're great Comment from : @2Cerealbox |
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