Deep Learning-All Optimizers In One Video-SGD with Momentum,Adagrad,Adadelta,RMSprop,Adam Optimizers

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In this video we will revise all the optimizers
02:11 Gradient Descent
11:42 SGD
30:53 SGD With Momentum
57:22 Adagrad
01:17:12 Adadelta And RMSprop
1:28:52 Adam Optimizer
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Опубликовано:

28 сен 2024

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Комментарии : 190

@tesla1772 3 года назад

This video cleared many doubts. I would suggest everyone to watch even if you have watched previous videos.

@techwithsolo 3 года назад

You are a very good teacher. I have been following your video since 2020. It has helped me to understand so many concepts in Machine and deep learning. I like your simplicity with respect to your teachings. Thanks a lot. Writing from Nigeria.

@shraddhaagrahari7519 2 года назад

I am becoming a fan of you .... I am very lazy person ... Never want to study but after Watching your videos . It feel good to learn .... Thank You Krish Sir....

@rajputjay9856 3 года назад

Mini-batch Gradient Descent. at each step, instead of computing the gradients based on the full train‐ ing set (as in Batch GD) or based on just one instance (as in Stochastic GD), Mini- batch GD computes the gradients on small random sets of instances called mini- batches. The main advantage of Mini-batch GD over Stochastic GD is that you can get a performance boost from hardware optimization of matrix operations, especially when using GPUs

@nishah4058 2 года назад

In earlier comment u told that stochastic took hole data

@RashiJakhar-w6r Год назад

you are the best teacher . i have seen many videos' but no one explain concepts so deep and clearly.

@vikashdas1852 3 года назад

I have always been confused with Optimizers in NN however this was the best resource available on internet that gave me an end to end clarity. Hatts off to Krish Sir.

@rajputjay9856 3 года назад

The main problem with Batch Gradient Descent is the fact that it uses the whole training set to compute the gradients at every step, which makes it very slow when the training set is large. At the opposite extreme, Stochastic Gradient Descent just picks a random instance in the training set at every step and computes the gradients based only on that single instance. Obviously this makes the algorithm much faster since it has very little data to manipulate at every iteration. It also makes it possible to train on huge training sets, since only one instance needs to be in memory at each iteration (SGD can be implemented as an out-of-core algorithm.) On the other hand, due to its stochastic (i.e., random) nature, this algorithm is much less regular than Batch Gradient Descent: instead of gently decreasing until it reaches the minimum, the cost function will bounce up and down, decreasing only on aver‐ age. Over time it will end up very close to the minimum, but once it gets there it will continue to bounce around, never settling down . So once the algo‐ rithm stops, the final parameter values are good, but not optimal.

@adityabobde2882 3 года назад

Randomness is good to escape from local optima but bad because it can never settle at minima, to overcome this you can use Learning Scheduler which reduces the steps as we approach global minima.

@nishah4058 2 года назад

Whole data is feed in gd ...in stochastic we took 1-1 record in batch we batches the records??we call it stochastic as there is more randomness in it as 1-1 data is feed so randomness and noise...

@hemamaliniveeranarayanan9901 4 месяца назад

nice explanation Krish sir... wonderfully explained all the optimizers.

@gourab469 2 года назад

Beautifully explained and taught. Hats off!

@sumitkumarsharma4004 2 года назад

This is called amazing. I went through paper .I was not able to grap the concept but your teaching skill is amazing. Thanks for this video. I request for yogi algorithm video.

@yogeshkadam8160 3 года назад

अत्यंत महत्वाचा विडिओ सर..❤

@shahrukhsharif9382 3 года назад

hi sir, you made a great video. But in the Sgd with momentum equation, some error is there. in the video, you explained this equation w_t = w_t-1 - (learning_rate)*dw_t dw_t = B* dw_t-1 + (1-B)*dl/dw_t-1 but in this eqaution it's sholud not be dl/dw_t-1. it will be dl/dw_t So correct equation is dw_t = B* dw_t-1 + (1-B)*dl/dw_t

@ruthvikrajam.v4303 2 года назад

I guess u are absolutely correct

@sivabalaram4962 2 года назад

Try to watch full video, that would be better for understanding every optimizer... thank you so much krish Naik Ji 👍👍👍

@kishlayraj4219 3 года назад

I liked the video, it was excellent. The only problem is that it had a lot of ads and really got frustrated at a point.

@pravalikadas5496 2 года назад

You are a fantastic teacher Krish. Simple.

@arjunsubramaniyan1675 3 года назад

About Bias correction in Adam, Just wanted to write about the need for it, So when we have B1 and B2 (Beta) , for the first iteration Both Momentum parameter and learning rate parameter will be zero so Sdw(1) will end up being very small and since Sdw will be in the denominator while updating the new weight w1, this will give a really huge change for the initial iterations and in the paper it has been mentioned that due to this bias of having zero (For the first iteration), the loss might not reduce over time so, we the authors proposed a bias correction, where we do a weighted average instead of a simple moving average Sdw(t)corrected = Sdw(t)/1-(B2^t) where t is the number of iterations so if you notice, for the first few iterations the value of bias corrected Sdw(t) is different from Sdw(t) but as t increases Sdw(t)corrected is equal to Sdw(t) as the denominator becomes 1, thus this correction removes the bias created by using Vdw(0)=0 and Sdw(0)=0

@arjunsubramaniyan1675 3 года назад

Also, Wonderful explanation @Krish Naik sir, I learnt all about optimizers from your video and wanted to find out the reason behind bias correction and ended up finding this, Awesome explanations!!

@aditisrivastava7079 3 года назад

Thanks for the nice explanation

@mohamedbadi8875 5 месяцев назад

Thank you Mr Krish, your work inspired me,now i understand optimizers

@MrAyandebnath 3 года назад

Very informative and best video on youtube to understand details of all optimization techniques. Thanks @Krish Naik..I became your admirer

@francisegah6115 Год назад

Krish is fastly becoming my favorite teacher

@merv893 2 года назад

This guys great, repeats himself so you can’t forget

@sharanpreetsandhu3215 3 года назад

Amazing explanation Krish. You teach in a very simple manner. I respect your skills. You made deep learning concepts so easy. Keep doing the good work. Thank you so much and All the Best.

@sur_yt805 3 года назад

One of best method to teach thanks alot so simple so concise and every point is important

@tagoreji2143 2 года назад

Thank you so much Sir for this Educative Video. VERY VERY VERY EDUCATIVE. THANKS A LOT Sir 🙏 Not even my Course faculty had taught like this And the words u spoke @56:00 increased my Respect towards You Sir That's 💯% true.We should be respectful to the Researchers and Every one behind what we Learning

@BalaguruGupta 3 года назад

This is a well explained video to understand Optimizers. Thanks a lot Krish!

@anandhiselvi3174 3 года назад

Sir please do video on svm kernels

@hashimhafeez21 3 года назад

We are understanding beacuse you teach brilliantly..

@kcihtrakd 2 года назад

Really nice way of teaching Krish. Thank you so mcuh

@bipulsingh6232 Год назад

tq so much in one viedeo my total unit finished

@Hari-xr7ob 3 года назад

Time taken to update the weights is the same in all the three cases (GD, SGD and Mini Batch SGD). ONly forward propagation will take different times in these three cases

@anupsahoo8561 3 года назад

This is really awesome video. The maths is explained really in details. Thanks

@meenalpande Год назад

Really very great effort .. Thank you sir

@harshvardhanagrawal Месяц назад

@1:39:40 Why is this equation being changed? is it not for weight? If its bias correction, then should we not update only the Vdb? Why Vdw? Should it rather not be just called correction? Why is the term Bias used?

@voidknown2338 3 года назад

Very good one by one from basic to advance and one related to other gradually increasing to the best optimizer Adam❤️

@LakshmiDevilifentravels 11 месяцев назад

Hello Krish I'm a Research Scholar and I was looking for some good explanations and luckily found your video and you made it clear in the first shot Forget the formulas why one after the other algorithms came into picture was really much clear with math intuitions. Will save your video for future reference. Thank you Kish . Appreciate your works.

@mhadnanali 2 года назад

I think you are wrong about SGD. Stochastic stands for random so it means, it will choose random inputs and perform GD on them. so it will converge faster. it does not mean it is iterate one by one.

@ishantsingh3366 3 года назад

You're too awesome to exist ! Thanks a lot man !!

@kanhataak1269 3 года назад

Amazing video Sir.

@richadhiman585 3 года назад

Thank you sir...you have explained everything very nicely... excellent work..🙏❤️

@dr.ratnapatil9272 3 года назад

Awesome explanation

@joelbraganza3819 3 года назад

Thanks for explaining it simply and easily.

@pruattea0302 3 года назад

Respect Sir, such an amazing explanation with super crystal clear. Super Thank you

@wahabali828 3 года назад

Sir in live session you specify Sdw in rmsprop and in recorded videos you specify Wavg both are same or different?

@deelipvenkat5161 2 года назад

very well explained 👍

@yathishs1895 3 года назад

@krish Naik ok then the previous video on sgd with momentum was wrong and this explanation is correct?

@akashkumar-ni9ec Год назад

what an effort! stunning

@benbabu9404 Год назад

in the equation of alpha_t in adagrad, is the limit of i from 0 to t ? If so, it means for each iteration value of LR varies from initial value to a minimum in each iteration. Isn't is logical for the LR to vary for each epoch?

@harshvardhanagrawal Месяц назад

@37:49 what is this a1 a2 a3 data and why are you replacing it with dL/dw or dL/db @45:06?

@SouhardyaDasChowdhury Год назад

Why the value of alpha (that square term) would skyrocket ?? Asking in terms of adagrad(learning rate decay one)

@ramakrishnayellela7455 6 месяцев назад

Is weights gets updated for every iteration or for every epoch.

@tejas260 3 года назад

For those who are concerned, the equation of adom had beta1, beta2, beta, it won't be beta there, it will be beta1 for vt equations, and beta2 for st equations. So without bias correction we'll have just 2 hyper-parameters, beta1 and beta2.

@sweetisah735 3 года назад

After seeing this video, m getting dizzy. u taught very well but my mind is dancing with fear after seeing so much.

@scienceandmathbyankitsir6403 2 года назад

Plz explain Nadam, ftrl etc too

@adithyajob8728 2 месяца назад

Awesome !

@harshkhandelwal2974 3 года назад

I don't know what to say, so I subscribed!! nice video :)

@arjyabasu1311 3 года назад

Thank you so much for this live session !!

@thepresistence5935 3 года назад

SGD with momentum formula changes(old video) to (new video) confused but, I got we do exponential moving average in sgd with momentum.

@OmkarYadavDhudi 3 года назад

Hi Krish, can do same thing for ML techniques

@ppsheth91 3 года назад

Amazing video krish sir..!

@rasikai102 3 года назад

Sir, thank you so much for this story. It has cleared all my doubts. Maths behind this all is so interesting. Sir, but you have not explained RMS Prop anywhere. Not on this mega video of optimizers and nor on this link Tutorial 16- AdaDelta and RMSprop optimizer. Can you please walk us through RMS once or a short video on it? Even in this video you have directly stated about RMSProp but we dunno why RMS was introduced like we know that for other optimizers. Looking forward to this. Also, sir in this Tutorial 16- AdaDelta and RMSprop optimizer:"gamma is taken and terminology is Weighted average (Wavg), wherein this current mega optimizer video you are saying ""beta and Sdw (replaced by Wavg) ". We are learning sir this will confuse us all the more. Please use same signs/terminology all over the videos.

@nehabalani7290 3 года назад

adadelta is RMS prop

@oss1996 3 года назад

Great brother, doing excellent work 👍👍

@Stenkyedits 2 года назад

wouldnt make more sense if adagrad alfa_t = sum(i=0->t)(dL/dw_(t-i)) ? since N will be decreasing more smoothly according to the W change in each iteration?

@stipepavic843 2 года назад

respect!!!! subbed

@KOTESWARARAOMAKKENAPHD Год назад

sir i watched SGD with Momentum,Adagrad,Adadelta,RMSprop,Adam Optimizers videos, but i need two more topics -Nesterov accelerated GD, Nadam videos

@gokulnath4297 Год назад

10k records, 1000 samples in one batch , ie)10 iterations(epoch/batch size)

@AnilVeni 3 года назад

Sir please can you make the video on sailfish optimization

@shantanu556 11 месяцев назад

Thanks sir, helped me.

@wilsvenleong96 2 года назад

For the last point on bias correction, could you or anyone please explain its purpose. Thank you!

@ArunKumar-sg6jf 8 месяцев назад

Alpha t is wrong u giving wt but it shold wt-1 for adgrad

@raghvendrapal1762 3 года назад

Very nice video, one doubt here, I want know in each epoch, are we using weights of last completed epoch or just randomly generating it in each epoch?

@hamedmajidian4451 2 года назад

However, I'm not sure is the definition of SGD in your stream is correct.

@IndustrialAI Год назад

Can anyone share the link of the deep learning notes of Krish sir?

@aDarkDay 11 месяцев назад

thanks. well taught : )

@DenorJanes 3 года назад

Hello Krish, everything in the video is very well explained, but I didn't understand why do we need bais correction for the Adam formula and why it uses b1 and b2, which depend on the timestamp t? b1 and b2 seemed to be constant values through out your explanations, so it doesn't make sense to me why would they depend on time in that formual... But just in case, b1(t) and b2(t) equal to b1 and b2, then the end formual would look like: vdw = vdw * (b1/(1-b1)) + dl/dw. Where in case of b1 = 0.95, we would get: vdw = vdw * 19 + dl/dw, so it looks like a scaled version of the original fromula, where all variables are given an additional weight. Could you please comment on that?

@rajak7410 3 года назад

Understood 100% , I got it in a single time. Excellent explanation

@rafibasha1840 2 года назад

What’s the disadvantage of RMSprop krish

@Rahul_Singh_Rajput_04 2 года назад

hi sir first of all thankyou for providing such a valuable education , sir where we can get this notes.

@hichamkalkha5847 2 года назад

Thank u bro! Question : should i retain that : GD=>One epoch leads to underfitting. SGD => require more resources RAM etc. (comp. explos.) ?

@sriramvaidyanathan5094 10 месяцев назад

can give me a small hint on what is the beta exactly

@sumaiyachoudhury7091 10 месяцев назад

at 1:32:27 , it should be V_db (not w)

@shubhamchoudhary5461 3 года назад

Thank you sir....you are amazing

@louerleseigneur4532 3 года назад

Thanks Krish

@Amankumar-by9ed 3 года назад

One of the best video about optimization algorithm.❤️

@haripandey5276 2 года назад

Awesome

@harshvardhanagrawal Месяц назад

@1:32:30 I think it should be (1-β1), and @1:33:30 I think it should be (1-β2), no?

@mohamedyassinehaouam8956 2 года назад

very interesting

@hamedmajidian4451 2 года назад

you rock!!!

@nhactrutinh6201 3 года назад

There are one matrix dLoss_dw at each layer. So there are many layers. ADAM and other optimizer occurs at each layer?

@aayushguptaaa Год назад

Yes

@miraagarwal632 3 года назад

Lot of noise in the form of advertisement in ur video. Please try to apply gradient descent or batch gradient descent there

@uonliaquat7957 3 года назад

Would you mind providing this white board sheet ?

@nhactrutinh6201 3 года назад

I think AdaDelta or RMSProp does not change learning rate as in the video. It adjusts dLoss_dw of each neron of each weight. S is the same size with Weight matrix so this AdaDelta or RMSProp does not change learning rate.

@bhavesh7505 8 месяцев назад

Is AdaDelta and RMSProp same?

@anusuiyatiwari1800 3 года назад

Thank u so much sir...

@InovateTechVerse 3 года назад

You are my guru.

@RAZZKIRAN 3 года назад

can we use PSO ?

@anuragmishra2032 2 года назад

What happens if we apply the momentum gd again after first smoothing once. is it viable? does it take more comoutation time? will it make the path more smooth? @krishnaik

@Bunny-yy6fo 3 года назад

Hi, can anyone explain maths behind SGD with momentum for the graph to become smooth, means how the graph is getting smooth if we apply SGD with momentum?