HI Krish.. dL/dW'11= should be [dL/dO21. dO21/dO11. dO11/dW'11] + [dL/dO21. dO21/dO12. dO12/dW'11] as per the last chain rule illustration. Please confirm

@rahuldey6369

3 жыл бұрын

...but O12 is independent of W11,in that case won't the 2nd term be zero?

@RETHICKPAVANSE

3 жыл бұрын

wrong bruh

@ayushprakash3890

3 жыл бұрын

we don't have the second term

@Ajamitjain

3 жыл бұрын

Can anyone clarify this? I too have this question.

@grahamfernando8775

3 жыл бұрын

@@Ajamitjain dL/dW'11= should be [dL/dO21. dO21/dO11. dO11/dW'11]

I have been taking a well-known world-class course on AI and ML since the past 2 years and none of the lecturers have made me so interested in any topic as much as you have in this video. This is probably the first time I have sat through a 15-minute lecture without distracting myself. What I realise now is that I didn't lack motivation or interest, nor that I was lazy - I just did not have lecturers whose teaching inspired me enough to take interest in the topics, yours did. You have explained the vanishing gradient problem so very well and clear. It shows how strong your concepts are and how knowledgeable you are. Thank you for putting out your content here and sharing your knowledge with us. I am so glad I found your channel. Subscribed forever.

many years ago in the college I was enjoy watching videos from IIT - before the mooc area, India had and still have many good teachers ! It brings me joy to see that again. Seems Indians have a gene of pedagogy

I hardly comment on videos, but this is a gem. One of the best videos explaining vanishing gradients problems.

I like how you explain and end your class "never give up " It very encouraging

@manishsharma2211

3 жыл бұрын

Yes

Great stuff! Finally understand this. Also loved it when you dropped the board eraser

Sir or As my Indian Friends say, "Sar", you are a very good teacher and thank you for explaining this topic. It makes a lot of sense. I can also see that you're very passionate however, the passion kind of makes you speed up the explanation a bit making it a bit hard to understand sometimes. I am also very guilty of this when I try to explain things that I love. Regardless, thank you very much for this and the playlist. I'm subscribed ✅

@amc8437

3 жыл бұрын

Consider reducing playback speed.

Thank you sir for making this misleading concept crystal clear. Your knowledge is GOD level 🙌

I am amazed by the level of energy you have! Thank you :)

Very well explained. I can't thank you enough for clearing all my doubts!

Kudos sir ,am working as data analyst read lots of blogs , watched videos but today i cleared the concept . Thanks for The all stuff

Thank you so much. The amount of effort you put is commendable.

so far best explanation about vanishing gradient.

You are teaching better than many other people in this field.

Thank you for all the effort you put into your explanations, they are very clear!

Kudos to your genuine efforts. One needs sincere efforts to ensure that the viewers are able to understand things clearly and those efforts are visible in your videos. Kudos!!! :)

oh my god you are a good teacher i really fall in love how you explain and simplify things

Love your videos, I have watched and taken many courses but no one is as good as you

Thank you very much, I was wandering around the internet to find such an explanatory video.

I just want to add this mathematically, the derivative of the sigmoid function can be defined as: *derSigmoid = x * (1-x)* As Krish Naik well said, we have our maximum when *x=0.5*, giving us back: *derSigmoid = 0.5 * (1-0.5) --------> derSigmoid = 0.25* That's the reason the derivative of the sigmoid function can't be higher than 0.25

@ektamarwaha5941

4 жыл бұрын

COOL

@thepsych3

4 жыл бұрын

cool

@tvfamily6210

4 жыл бұрын

should be: derSigmoid(x) = Sigmoid(x)[1-Sigmoid(x)], and we know it reaches maximum at x=0. Plugging in: Sigmoid(0)=1/(1+e^(-0))=1/2=0.5, thus derSigmoid(0)=0.5*[1-0.5]=0.25

@benvelloor

4 жыл бұрын

@@tvfamily6210 Thank you!

@est9949

3 жыл бұрын

I'm still confused. The weight w should be in here somewhere. This seems to be missing w.

I must say this, normally I am kinda person who prefers to study on own and crack it. Never used to listen to any of the lectures till date because I just don't understand and I dislike the way they explain without passion(not all though). But, you are a gem and I can see the passion in your lectures. You are the best Krish Naik. I appreciate it and thank you.

I like the way you explain things, making them easy to understand.

Krish...you rock brother!! Keep up the amazing work!

One of the best vedio on clarifying Vanishing Gradient problem..Thank you sir..

Marana mass explanation🔥🔥. Simple and very clearly said.

Nice presentation..so much helpful...

Very nice way to explain. Learned from this video- 1. Getting the error (Actual Output - Model Output)^2 2. Now We have to reduce an error i.e Backpropagation, We have to find a new weight or a new variable 3. Finding New Weight = Old weight x Changes in the weight 4. Change in the Weight = Learning rate x d(error / old weight) 5. After getting a new weight is as equals to old weight due to derivate of Sigmoid ranging between 0 to 0.25 so there is no update in a new weight 6. This is a vanishing gradient

Krish.. You are earning a lot of Good Karmas by posting such excellent videos. Good work!

The way you explain is just awesome

Overall got the idea, that you are trying to convey. Great work

Great tutorial man! Thank you!

One of the best explanations of vanishing gradient problem. Thank you so much @KrishNaik

Thank you so much, great quality content.

So happy I found this channel! I would have cried if I found it and it was given in Hindi (or any other language than English)!!!!!

Great explanation, Thank you!

Thank you, Krish SIr. Nice explanation.

you sir made neural network so much fun!

your videos are very helpful ,good job and good work keep it up...

Very nice video sir , you explained very well the inner intricacies of this problem

I'm lucky to see this wonderful class.. Tq..

Very clear explanation, thanks for the upload.. :)

Tommorow I have interview, clearing all my doubts from all your videos 😊

very simple and nice explanation . I understand it in first time only

Appreciate your way of teaching which answers fundamental questions.. This "derivative of sigmoid ranging from 0 to 0.25" concept was nowhere mentioned.. thanks for clearing the basics...

@mittalparikh6252

3 жыл бұрын

Look for Mathematics for Deep Learning. It will help

crystal clear explanation !

great video! thank you so much!

You just earned a +1 subscriber ^_^ Thank you very much for the clear and educative video

Thank you sir for your amazing video. that was great for me.

Nice video Krish.Please make practicle based video on gradient decent,CNN,RNN.

very nice explanation,,great :)

super video...extremely well explained.

Thank youuuu, its really great:)

Thanks a lot sir for the wonderful explanation :)

You are just amazing. Thnx

Helped a lot....thanks

Very nice series... 👍

best explanation. Thanks man

Thank you thank you thank you sir infinite times🙏.

great video and great explanation

hats off to you sir,Your explanation is top level, THnak you so much for guiding us...

@DEVRAJ-np2og

15 күн бұрын

do u completed his full playlist?

You should get Oscar for your teaching skills.

that was very well explained

very good explanation.

Thank you so much for this

Great efforts Sir

thanks sir you really hepled me

Very nice now i understand why weights doesn't update in RNN. The main point is derivative of sigmoid is between 0 and 0.25. Vanishing gradient is associated with only sigmoid function. 👋👋👋👋👋👋👋👋👋👋👋👋

Great Lecture

This video is amazing and you are amazing teacher thanks for sharing such amazing information Btw where are you from banglore?

excellent explanation sir

I am doing deep learning specialization, feeling that this is much better than that

Thank you so much

very well explained 100/100

Thank you !!

I understood it. Thanks for the great tutorial! My query is: weight vanishes when respect to more layers. When new weight ~= old weight result becomes useless. what would the O/P of that model look like (or) will we even achieve global minima??

Thank you !

nice explanation.

excellent video

Excellent 👌

Good job bro as usual... Keep up the good work.. I had a request of making a video on implementing back propagation. Please make a video for it.

@krishnaik06

5 жыл бұрын

Already the video has been made.please have a look on my deep learning playlist

@hokapokas

5 жыл бұрын

@@krishnaik06 I have seen that video but it's not implemented in python.. If you have a notebook you can refer me to pls

@krishnaik06

5 жыл бұрын

With respect to implementation with python please wait till I upload some more videos

nice explanation

Hats Off Brother

you are legend nayak sir

As usual extremely good outstanding... And a small request can expect this DP in coding(python) in future??

@krishnaik06

5 жыл бұрын

Yes definitely

Sir thank u for teaching us all the concepts from basics but just one request is that if there is a mistake in ur videos then pls rectify it as it confuses a lot of people who watch these videos as not everyone sees the comment section and they just blindly belive what u say. Therefore pls look into this.

nice explaination

Very interesting

Understood completely! If weights hardly change, no point in training and training. But I have got a question, where can I use this knowledge and understanding I just acquired ?

Nice expalnation sir

Hello sir, why the chain rule explained in this video is different from the very last chain rule video. kindly clearly me and thanks for such an amazing series on deep learning.

Guruvar ko pranam🙏

Great-

superb

Thanks krish .Video was superb but I am having apprehension I might get lost somewhere .Please provide some reading reference regrading this topic considering as a beginner.Cheers

Sir i'm really confusing about the actual y value please can you tell about that. i thought it would be our input value but here input value is so many with one predicted output

May god bless you ..

@krish: thanks for the wonderful lessons on the neural network. may I request you to correct the equation using some text box on the video as this will have intact information that you would like to pass on

Very well explained. Vanishing gradient problem as per my understanding is that, it is not able to perform the optimizer job (to reduce the loss) as old weight and new weights will be almost equal. Please correct me, if i am wrong. Thanks!!

Derivative of loss with respect to w11 dash you specified incorrectly, u missed derivative of loss with respect to o21 in the equation. Please correct me if iam wrong.

@sekharpink

5 жыл бұрын

Please reply

@ramleo1461

4 жыл бұрын

Evn I hv this doubt

@krishnaik06

4 жыл бұрын

Apologies for the delay...I just checked the video and yes I have missed that part.

@ramleo1461

4 жыл бұрын

@@krishnaik06Hey!, U dnt hv to apologise, on the contrary u r dng us a favour by uploading these useful videos, I was a bit confused and wanted to clear my doubt that all, thank you for the videos... Keep up the good work!!

@rajatchakraborty2058

4 жыл бұрын

@@krishnaik06 I think you have also missed the w12 part in the derivative. Please correct me if I am wrong

Should we again add bias to the product of the output from the hidden layer O11, O12 and weights W4, W5?