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Tutorial 7- Vanishing Gradient Problem
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- Опубликовано: 21 июл 2019
- Vanishing Gradient Problem occurs when we try to train a Neural Network model using Gradient based optimization techniques. Vanishing Gradient Problem was actually a major problem 10 years back to train a Deep neural Network Model due to the long training process and the degraded accuracy of the Model.
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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
...but O12 is independent of W11,in that case won't the 2nd term be zero?
wrong bruh
we don't
have the second term
Can anyone clarify this? I too have this question.
@@Ajamitjain dL/dW'11= should be [dL/dO21. dO21/dO11. dO11/dW'11]
I like how you explain and end your class "never give up " It very encouraging
Yes
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 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.
I hardly comment on videos, but this is a gem. One of the best videos explaining vanishing gradients problems.
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 ✅
Consider reducing playback speed.
Thank you sir for making this misleading concept crystal clear. Your knowledge is GOD level 🙌
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
COOL
cool
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
@@tvfamily6210 Thank you!
I'm still confused. The weight w should be in here somewhere. This seems to be missing w.
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
so far best explanation about vanishing gradient.
You are teaching better than many other people in this field.
I am amazed by the level of energy you have! Thank you :)
Kudos sir ,am working as data analyst read lots of blogs , watched videos but today i cleared the concept . Thanks for The all stuff
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...
Look for Mathematics for Deep Learning. It will help
oh my god you are a good teacher i really fall in love how you explain and simplify things
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.
Great stuff! Finally understand this. Also loved it when you dropped the board eraser
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!!! :)
One of the best vedio on clarifying Vanishing Gradient problem..Thank you sir..
Thank you, Krish SIr. Nice explanation.
Thank you very much, I was wandering around the internet to find such an explanatory video.
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)!!!!!
Marana mass explanation🔥🔥. Simple and very clearly said.
Nice presentation..so much helpful...
Overall got the idea, that you are trying to convey. Great work
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 explanation, Thank you!
Thank you so much. The amount of effort you put is commendable.
Thank you so much, great quality content.
Very well explained. I can't thank you enough for clearing all my doubts!
Love your videos, I have watched and taken many courses but no one is as good as you
I'm lucky to see this wonderful class.. Tq..
One of the best explanations of vanishing gradient problem. Thank you so much @KrishNaik
very simple and nice explanation . I understand it in first time only
I like the way you explain things, making them easy to understand.
I am doing deep learning specialization, feeling that this is much better than that
crystal clear explanation !
Thank you for all the effort you put into your explanations, they are very clear!
Thank you sir for your amazing video. that was great for me.
Krish.. You are earning a lot of Good Karmas by posting such excellent videos. Good work!
Tommorow I have interview, clearing all my doubts from all your videos 😊
Krish...you rock brother!! Keep up the amazing work!
thanks sir you really hepled me
great video! thank you so much!
You should get Oscar for your teaching skills.
Thank you thank you thank you sir infinite times🙏.
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.
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.
Please reply
Evn I hv this doubt
Apologies for the delay...I just checked the video and yes I have missed that part.
@@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!!
@@krishnaik06 I think you have also missed the w12 part in the derivative. Please correct me if I am wrong
The way you explain is just awesome
Very nice video sir , you explained very well the inner intricacies of this problem
Very clear explanation, thanks for the upload.. :)
you sir made neural network so much fun!
hats off to you sir,Your explanation is top level, THnak you so much for guiding us...
do u completed his full playlist?
Nice video Krish.Please make practicle based video on gradient decent,CNN,RNN.
Great tutorial man! Thank you!
Great Lecture
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??
that was very well explained
Helped a lot....thanks
Very nice series... 👍
you are legend nayak sir
Thank you so much for this
Great efforts Sir
very well explained 100/100
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.
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 ?
As usual extremely good outstanding...
And a small request can expect this DP in coding(python) in future??
Yes definitely
your videos are very helpful ,good job and good work keep it up...
nice explanation.
you teach better than ivy league professors. what a waste of money spending $$$ on college.
excellent video
You just earned a +1 subscriber ^_^
Thank you very much for the clear and educative video
Hi krish
everyone says that Wnew = Wold - n * dL/dWold
theoritically we know that dL/dWold means slope
where as in practical scenario
L is a single scalar value
Wold is also a single scalar value
Then how dL/dWold is calculating ???
And also coming to the activation function , you are explaining it theoritically ,
can you explain it by taking practical values ? , and don't tell it by taking predefined function or module ,
bcz we know how to find a module and import it and how to use it ,
but we don't know practical
This video is amazing and you are amazing teacher thanks for sharing such amazing information
Btw where are you from banglore?
nice explanation
Very interesting
very nice explanation,,great :)
Thank you so much
Nice expalnation sir
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.
Already the video has been made.please have a look on my deep learning playlist
@@krishnaik06 I have seen that video but it's not implemented in python.. If you have a notebook you can refer me to pls
With respect to implementation with python please wait till I upload some more videos
Hats Off Brother
Thanks a lot sir for the wonderful explanation :)
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!!
Thank youuuu, its really great:)
great video and great explanation
Gradient Descent will be applied on Cost function right ?-1/m Σ (Y*log(y_pred) + (1-y)* log(1-y_pred))... in this case if they had applied on the activation function, how the algo will come to global minima.
Thank you !!
May god bless you ..
superb
Thanks krish
Guruvar ko pranam🙏
Could you please explain why bias is needed in neural networks along with weights?
it is because when you want to control or fix the output of a given neuron within a certain range, for example, if the neuron is always giving inputs between 9 and 10, you can put a bias =-9 so as to make the neuron output between 0 and 1
so relu function is the best solution for deleting vanishing gradient descent
super video...extremely well explained.
You are just amazing. Thnx
excellent explanation sir
Sir you are saying derivative of sigmoid is 0 to 0.25. I understand it.
But how that will imply derivative of O21 /derivative of 011 should be less than 0.25.
Could you please help me understand that assumption
He agreed that he did it wrong subconsciously
I found his comment somewhere in this chat
The output of every neuron in a layer is the Sigmoid of weighted sum of input. Since sigmoid is applied as the activation function in every neuron(here O21 is output after applying sigmoid function), the derivative should be between 0 and 0.25.
best explanation. Thanks man
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
Can someone please explain why the derivative of each parent layer reduces ? i.e why does layer two have lower derivative of O/P with respect to its I/P?