Best explanation of cost function, we learned it as masters students and the course couldnt explain it as well.. simply brilliant

I never understood what is a gradient descent and a cost function is until I watch this video 🙏🙏

I have seen many teachers explaining the same concept, but your explainations are next level. Best teacher.

Why am I not surprised with such a lucid and amazing explanation of cost function, gradient descent,Global minima, learning rate ...may be because watching you making complex things seems easy and normal has been one of my habit. Thank you SIR

How can I not say that you are amazing !! I was struggling to understand the importance of gradient descent and u cleared it to me in the simplest way possible.. Thank you so much sir :)

Best video on youtube to understand the intution and math(surface level) behind Linear regression. Thank you for such great content

Awesome!! Cleared all doubts seeing this video! Thanks alot Mr. Krish for creating indepth content on such subject!

Thanq so much for all your efforts.... Knowledge, rate of speech and ability to make thing easy are nicest skill that you hold...

The best I've come across on gradient descent and convergence theorem

Really awesome video , so much better than many famous online portals charging huge amount of money to teach things.

Really thanks you krish. you just cleared my doubts on cost function and gradient descent. First I saw Andrew Ng class but have few doubts after seeing you video. Now its crystal clear.. Thank You...

For those who are confused. The convergence derivative will be dJ/dm.

@tusharikajoshi8410

Жыл бұрын

what's J in this? Y values? I'm super confused about this d/dm of m, cz it would be just 1. and m I think is just total number of values. Shouldn't the slope be d/dx of y?

@mdmynuddin1888

Жыл бұрын

@@tusharikajoshi8410 it will be the cost or loss (J)

@mdmynuddin1888

Жыл бұрын

new(m) = m- d(loss or cost)/dm * Alpha(learning rate.

@suhasiyer7317

11 ай бұрын

Super helpful

@threads25

9 ай бұрын

I'dont think because it netwons method actually

This is the best stuff i ever came across on this topic !

Thank You Sir, You have explained everything about gradient Descent in the best possible easiest way !!

Thank you Soo much Krish. No where I could find such a detailed explanation You made my Day!

i knew the concept of Linear Regression but didn't know the logic behind it.. the way Line of Regression is chosen. Thanks for this!

I don't see a link on the top right corner for the implementation as you said in the end.

Great! Fantastic! Fantabulous! tasting the satisfaction of learning completely - only in your videos!!!!!

The video was really great. But I would like to point out that the derivative that you took for convergence theorem, there instead of (dm/dm) it should be derivative of cost function with respect to m . Also a little suggestion at the end it would have been helpful, if you mentioned what m was, total number of points or the slope of the best fit line. Apart from this the video helped me a lot hope you add a text somewhere in this video to help the others.

you just made the whole concept clear with this video,you are a great teacher

Best video on theory of linear regression! Thankyou soo much Krish!

every line you speak..so much important to understand ths concept......thank u

Best explanation of Linear Regression🙏🙏🙏.Simply wow🔥🔥

So beautifully explained...did not find anywhere this kind of clarity....keepnup the good work....

Such a great explanation of gradient descent and convergence theorem.

Please add the indepth math intution of other algorithms like logistic, random forest, support vector and ANN.. Many Thanks for the clearly explained abt linear regression

god bless you too sir, explained very well. basics helps to grow high level understanding

A small comment at 17:35. I guess it is Derivative of J(m) over m. In other words, the rate of change of J(m) over a minute change of m. That gives us the slope at instantaneous points, especially for non linear curves when slope is not constant. At each point of "m, J(m)", Gradient descent travels in the opposite direction of slope to find the Global minima, with the smaller learning rate. Please correct me if I am missing something. Thanks for a wonderful video on this concept @Krish, your videos are very helpful to understand the Math intuition behind the concepts, I am a super beneficiary of your videos, Huge respect!!.

Watched this video 3 times back to back .Now its embaded in my mind forever. Thanks Krish , great explanation !!

Thankyou for this awesome explanation!

This maths is same as coursera machine learning courses Thank you sir for this great content ..

Your explanations are the clearest!!!

Before watching this video I was struggling with the concepts exactly like you were struggling in plotting the gradient descent curve. ☺️Thanks for explaining this beautifully.

one of the best explanation so far :)

you are ultimate, got answers to some many questions, video is good.

Oh my gosh this is awesome tutorial I ever seen God bless you sir🤩🤩

Yaar you nailed it man after watching sooo many videos i had some Idea , By Finishing your Video now i m completely clear 😍😍😍😍

@jagdishsahu1118

4 жыл бұрын

Right

This is a really good explanation for Linear Regresison, Krish.. looking forward to check out more of your videos..thanks and keep going!!

Thanks so much sir.. you're doing good for the community

Thank you my friend, you are a great teacher!

Hi Krish, That was an awesome explanation for the maths used for linear regression, especially for the cost function. Can you make a video on 5 assumptions of linear regression and also explain the assumptions in detail.

Sir, you are outstanding. Please keep it up

my god that was clear as crystal...thanks krish

Thank you so much, Krish!

No one can find easiest explanation of gradient descent on youtube. This video is the exception.

I knew that their will be an Indian that can make all the stuffs easy !! Thanks Krish

Value of the video is just undefinable! Thanks a lot :)

Thankyou sir...Get to learn so much from you.

Hi krish, that was an awesome explanation of Gradient Descent. With respect to finding the optimal slope. But in linear regression both slope and the intercept are tweakable parameters, how do we achive the optimal intercept value in linear regression.

Great sir. Love this video

Thank you Krish bhaiya!

Loved it. Thanks Krish.

Implementation part: Multiple linear Regression - kzread.info/dash/bejne/Z6aq0M6Th93VqJs.html Simple linear Regression - kzread.info/dash/bejne/d2Gs0o-Mmsm1g7w.html

Really great sir. I very much thank you sir for this clear explanation

As always Krish very well explained!!

lovely! love it.

I had so much difficulty in understanding gradient descent but after this video It's perfectly clear

@muralimohan6974

3 жыл бұрын

Bro, how we update the slope

Hi . Can you please do a video about the architecture of machine learning systems in real world . How does really work in real life .for example how hadop (pig,hive) , spark, flask , Cassandra , tableau are all integrated to create a machine learning architecture. Like an e2e

Thank you! This video was so good!

Great Tut sir got things pretty quick with this video ty

Hi Krish, Thanks for the video. Some queries/clarifications required: 1. We do not take gradient of m wrt m. That will always be 1. We take the gradient of J wrt m 2. If we have already calculated the cost function J at multiple values of m, then why do we need to do gradient descent because we already know the m where J is minimum 3. So we start with an m , calculate grad(J) at that point and update m with m' = m - grad(J)* learn_rate and repeat till we reach some convergence criteria Please let me know if my understanding is correct.

@slowhanduchiha

3 жыл бұрын

Yes this is correct

@vamsikrishna4107

3 жыл бұрын

I think we have to train the model to reach that min. loss point while performing grad. descent in real life problems.

@shreyasbs2861

3 жыл бұрын

How to find best Y intercept ?

Best explanation. Thank you!

There's a little correction in Convergence Theorem: derivative of J(m) should be there in place of derivative of m in numerator.

@salmanjaved2816

3 жыл бұрын

Correct 👍

@xanderkristopher1412

2 жыл бұрын

sorry to be so off topic but does anyone know of a way to get back into an instagram account..? I was stupid lost my password. I love any tricks you can give me.

Thanks Krish u are helping alot

excellent video u are a champion man

This is super helpful!

Similar to Andrew NG course from coursera kind of revision for me 😊😊

@Gayathri-jo4ho

3 жыл бұрын

Can you please suggest me how to begin with in order to learn machine learning

@Gayathri-jo4ho

3 жыл бұрын

@@ArpitDhamija did you have knowledge on machine learning??if so, please suggest me I saw so many but I couldnt able to .

@shhivram929

3 жыл бұрын

@@Gayathri-jo4ho This playlist itself is a fantastic place to start, Or can enroll in this course "Machine Learning A-Z by krill eremenkrov" in udemy. The course will give you an intuitive understanding of the ML Algorithms. Then it's up to you to research and study the math behind each concept..Reff (kgnuggets, Medium, MachineLearningplus and lot more)

@Gayathri-jo4ho

3 жыл бұрын

@@shhivram929 thank you

@sarithajaligama9548

3 жыл бұрын

Exactly. This is the equivalent of Andrew Ng's description

Nice Explanation, I like this.

your videos are clear and easy to understand

Amazing explanation! I have one question, from where did you study all of this? Some books or the net?

great video sir, so lucid

you are my inspiration

best one sir thank you so much

really great explanation sir 😍

Thanks krish sir

good expplanation now clear all queries

simply great

We would also recommend your videos to our students!

This video is really helpful.

Excellent!!!!!

Very nice explanation. Thank you.

can you do more math intuition s please. These are very helpful. Thanks!

never found a better explaination

Thanks for all great prepared videos, I think you meant (deriv.J(m) / deriv(m)) at 17'.45", is it correct?

Excellent Explanation

Nice tutorial. Thank you

Sir No Words to explain simply super b

very well explained Thank you.

Great video I understood the concept

very nice video. Thanks Krish

Hi Sir, I am from cloud & DevOps background is it make sense to go & learn Ml AI, what path I can follow to become a dataops engineer or devops ml ai engineer.

amazing man!

It was an amazing video , thanks

It was really helpful. Difference between Stochastic Gradient Descent and Normal Gradient Descent?

Excellent explanation sir. I have started following your videos for all the ML related topics its very interesting. One doubt = In Gradient Descent, when slope is zero, M value will be considered as the slope of the best file line. I do not understand this. Can you please explain here? Thanks.

It would be great if you could suggest some best books for python programming?

Thank you sir ♥️

Great insight 👍

sir i can' find the simple regression and multiple regression video as u said and some videos are little jumbled its getting difficult to follow the videos and plz do explain the functionalities of each and every keyword or a inbuilt function when ur explaining the code...ofcourse ur explaining in a very good way but i faced a liitle problem while folllowing that practical implementation of univariate,multivariate,and bivariate analysis(there you have used FACETGRID function)..so will u plz expalin me what is the exact use of facetgrid...?

Finally I understood the perfect answer of gradient descent..