Entropy (Basics, Definition, Calculation & Properties) Explained in Digital Communication

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[@sreenidhiranganayaki7526]

thank you sir hope your channel becomes famous and reaches 1 million subscribers......

[@EngineeringFunda]

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[@manickam11]

Degree of randomness is best suited definition. Entropy become zero because of the coefficient. Good job. Nice lecture.

[@abhishekjha_here]

Underrated channel. Best contents of any Engineering branch subjects is available here. Guided by our very own Hitesh Dholakiya Sir🙏🏻🙏🏻🙏🏻 Thank You So Much

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@@EngineeringFunda definitely sir👍🏻

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Great work sir, you are the real hero

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THANK YOU, VERY HELPFUL

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[@prateek6502-y4p]

Let me clarify something any KZbin professor will not:So what are we waiting for.Let's enjoy the intuition part.Shall we then!....let's jump into it.Ok.... We know expectation of random variable X is E(X) = summation (xP(X=x))for every value of X.it gives you the mean value of a random variable.what is mean?.is it same as the term average?. Let's say if u flip a coin twice. And I ask whats the Expectation(mean) of getting head..by this I want to know how many heads per toss I expect from this. Then u assign random variable as the no of heads in each of 4 possible outcomes. Then u use the above formula and get the ans as 1 .i.e. E(X) = 1.this value basically means for tossing coin twice,U get 1 head per toss.but if u look at the outcomes they are HH,HT,TT,TH.the no of heads being 2,1,0,and 1 respectively. So to conclude the value 1 gives u 'mean' of no of heads for this particular event. Sorry my explanation is taking so long. But pls stick with me till the end . Now coming to entropy...entropy gives the average of information? No not really it actually gives u weighted sum of information. Now what is weighted average? Weight simply means how much chance of that value to occur.in the world of probability weight simply means Probability. Now u know entropy of any system is inversely proportional to the probability of information. But why? Because let's say instead of tossing a coin twice U toss it just once.now there are only 2 outcomes head or tail. So basically it means this sample space has less no of randomness unlike tossing a coin multiple times. And information does increase with randomness.but as the no of the outcomes increase, the probability of each of outcomes decreases being its inversely proportional.so coming to communication part u write information of each symbol as log1/P of that symbol. Now u remember the formula E(log1/p)= summation of(log1/p(s)* p(s.) ).isnt this formula the expected value(or mean value) of Entropy?.yes it is.just substitute X in E(X ) with log1/p(s). Where p(s ) is Probability of symbol s. At last ! u remember I told u Entropy is just weighted sum of information.Right?. look at the formula of entropy. In it information of each symbol is first multiplied with its weight(probability) and then added to other weighted information. So to conclude my biggest comment ever(more like lecture), go for the intuition don't just memorise stuff. At least Try..Then only Study will be fun. ; ))

[@bhaveshamarsingh1656]

Thank you mate. :-)

[@nusratjahan2731]

The way you described was too easy to understand, sir. Thanks sir

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[@Yashwanth]

Tq sir great men's channel

[@pavankumar-ml7yl]

Thanku you that was very helpful

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[@Serafhunter]

Thanks for the great lecture. Please provide feedback on the following points. 1. At 3:14 when P = 0, We get 0 * log(1) - log (0). Since log(0) is undefine, Won't this result in the equation in indeterminate instead of 0? Or Am I missing something here? 2. At 1:05, Instead of I1 + I2 .. + In, We need (I1 * P1 * n) + (I2 * P2 * n) ... (In * Pn * n). The n is cancelled with the denominator and we get H = Σ pi log(1/pi)

[@theflamecoreguy7929]

true

[@shrutikhandare3354]

Nice explaintion sir , you voice is same as my teacher guardian 😄

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[@MounikaReddy-f9j]

Sir could you please share the the topic properties of entropy 3rd property function sir please share

[@jatothkishore]

Nice explanation sir

[@EngineeringFunda]

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[@opbnl7483]

Well thanks for teaching us🙂

[@EngineeringFunda]

Your positive comments motivates me. Teachers like me just wants positive comment from student. Love from you guys means a lot to me. My goal is to create largest community of engineers in entire globe. Please help me by sharing this playlist with your friends.

[@javid659]

very nice

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[@kodieswaria528]

Nice video sir

[@vaddiharinatha2093]

Tq so much for ur work

[@virajadesai4462]

It was good but I suggest you to summerize all points from beginning.

[@EngineeringFunda]

Noted ✅

[@kalpanaverma1269]

Thank you ! 🙌

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[@thisissharief7651]

nice bro

[@ajithsdevadiga1603]

3rd property - entropy is a function of probability and symmetry.

[@kabandajamilu9036]

Thanks

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[@mamtakundal9617]

Thank you sir

[@Cloudbox-NaughtyBoi]

Love you girl ❤️

[@shreyatiwari5934]

Is there any other properties or just two because for 10 marks it is not enough I guess

[@EngineeringFunda]

It is sufficient dear

[@shreyatiwari5934]

@@EngineeringFunda Thank you sir

[@mayanksagar633]

THANK YOU SO MUCH SIR

[@aniketrajapure8251]

Sir in formula negative sign is missing ?

[@0sumitujjwal0]

Entropy is average information per symbol

[@EngineeringFunda]

Correct

[@prateek6502-y4p]

Not really more like Entropy is weighted (probability) sum of the information.

[@abhisheksingh-hm1gk]

Sir but what is symbol !

[@mrroy7814]

sir you did't cover all the entropy topic pls, make these video like conditional ,relative differential entropy ...etc

[@subramanyak6187]

I am from one of the top college in Bangalore Sir, We don't have any teacher like you. Point to be mentioned to all karnataka students sir Follows VTU syllabus only.

[@atharvakale5625]

plz improve the audio quality. There were random noises in between.

[@Pavan_pavan-0]

Video is not clarity

[@SHRIDHARAN1001]

This is a duplicate video in the playlist

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Let me check ✔

[@JatinKumar-ig6qy]

Pure concept ki lga di...nothing explained