Deep Learning 19: (1) Variational AutoEncoder : Introduction and Probability Refresher

[@TheSupermanMc]

Being Frustrated from not being able to understand things because its so hard to read papers or code when u have no prior experience , someone who is doing bachelors in technology , your lectures are absolute masterpiece , thanks a lot sir keep going

[@RezoanurRahman]

You sir are an absolute legend. To make us understand about Autoencoders you literally EXPLAINED probability from the most basic level of tossing a dice. Hats off

[@QueirozVini]

At first I struggled with the accent, but by the end of the video I was comfortable with it. Great lecture, dude. Thanks!

[@submagr]

Thank you very much, Sir. Your videos are very good and you explain everything from scratch covering every minute detail. Thank you.

[@victorariasvanegas7407]

Sir thank you for this series, is incredible, this will be a great help to understand the concept.

[@ziegelstein8244]

At about 12:45 you are saying it runs only from 1 to 4 for P(x) = \sum_{i=0}^4 P(x|y_i) P(y_i) but there are 5 y: y_1, ..., y_5 Why should it be only 4? x is spanning over all 5 y. Why is y_5 omitted?

[@cristerronaldosuwy7171]

yes sirrrr

[@angelazhang9082]

One of the best explanations I've seen. Thank you!!!

[@srinathkumar1452]

You are a magnificent teacher!

[@MrSushantsingh]

At 3:24 Ahlad just slipped into Hindi mode, instead of 1 to n, he said 1 '"se" n and changed to English immediately, quick loss function from brain to mouth man...LOL..Dude you should try uploading NLP content as well, it would be great.

[@Veerbasantreddy111]

Thank you Sir for providing detailed info of AE

[@angellaz3869]

well explained, hats off to you

[@basab4797]

Incredible, awesome

[@MindfulDiariesSofia]

this is the best explanation I could I find! Thank you very much for your help. :)

[@vivekmittal2290]

Simply Amazing ! Thanks for such lectures.

[@navidalemik9560]

Thanks for your amazing courses. Very easy to follow

[@unnikrishnanm4821]

really cool sir....mind blowing lectures ...keep going

[@CyberwizardProductions]

appreciate your lectures

[@MichaelD9669]

Great lecture! Thanks for such detailed, structured and easy explanation. I'm amazed! subscribed immediately)

[@ssshukla26]

Great explanation... thank you sir.

[@Darkev77]

Am I crazy or applying the conditional probability rule (or Bayes) on question 2 (15:11) doesn't work?

[@buttert5091]

Thanks so much, you are a legend!!

[@yuravasiliuk6460]

very careful explanation. thank you.

[@ankitsingh-xl7bo]

Thank you so much for this.

[@sudhirpol1895]

Very useful information. Can you please create a video on Discrete VAE? Thanks!

[@xruan6582]

Good tutorial. (5:18) are you sure we train the denoise autoencoder with input x(delta) and output x? is it still an autoencoder if input and output are different?

[@daiversetech8000]

Yes, Its an autoencoder but is not trying to learn identity function (Creating the input at output layer).

[@kshitijSpace]

Precise Explanation. Thanks :)

[@hfz.arslan]

great explaination can we get lecture notes please?

[@cooolway]

Thank you very much sir. You are amazing.

[@barkupatil]

In Variational Autoencoder. How the variable no. of bits are allocated for encoding.

[@VinayPrasadTamta-s8o]

Sir I think with the diagram X can not be union of call Yi events. It should be union of some events from Yi.

[@prakarshjain2796]

Great lecture! One question only. Why are we assuming p(x) (in P(y/X)) to be the union of N mutually exclusive event of y?

[@deepika1401]

Sir plz mention the lecture in which u hv explained the loss function at 3:20..im new to ur channel

[@benren9004]

For the theorem of total probability, should it be that yi is collectively exhaustive?

[@not_a_human_being]

highly appreciated!

[@DarioRomeroDeveloper]

Hi @Aklad Kumar, to join the channel it says the cost is 6,000 CLP/month. How much is a CLP unit equivalent to US $?

[@AhladKumar]

it's 7.4 USD

[@olabanjishonibare2684]

Hello Ahlad, thanks for the great content. I think there might be an error in the law of total probability. The X is an arbitrary event and not the union of the mutually exclusive event. Keep up the good work.

[@riffraff7358]

After watching the videos the VAE seems so easy to me. Can you please make a video on "Attention all you need" paper? Many Thanks.

[@prarabdh6295]

Ok

[@learningtogether5449]

Where Can I get the notebook?

[@jirokaze6380]

Perfect! Thanks :)

[@saadanwer2106]

At 16:44 , You have written the formula wrong

[@yasminelabiod6063]

il a oublié le log ?

[@shineshine9599]

Can i have ur lecture slides plzzzzz

[@MohitPatelMAI]

legend

[@shineshine9599]

hii one question did u explain convolutional neural network in any or your video? i couldn't find.

[@AhladKumar]

no its my plan in coming videos

[@shineshine9599]

@@AhladKumar....thank u so much!!! My research project is age regression/ progression by conditional adversial network...what is the best way to understand and implement that paper..i am learning so many things at one time and kind of lost the direction. If you could genuinely guide me what are the topics i need to learn in a manner to have good understanding of above mentioned paper that would be great!!!

[@ankitsingh-xl7bo]

Sir can you reply to my mail

[@guangxzhu4019]

这口音，太重了