What is great about this particular video is these concepts are explained well in many places like scattered dots , you connected the dots to paint the whole picture . an example for gradient descent included - very helpful .
@RanjiRaj183 жыл бұрын
Thank You for your valuable feedback 😊
@gabelster3729 Жыл бұрын
You just helped me understand hundreds of web pages that talked about topics with no order. Thank you
@donaldngwira2 жыл бұрын
You are such a great teacher. Concepts are clearly explained beginning with the basics and slowly easing into the most advanced level. Thank you
@RanjiRaj182 жыл бұрын
You're very welcome!
@subramaniarumugam69022 жыл бұрын
My god you are perfect I think your work should reach more audience your best and clear than the renowned ML yputubers. Applause Ranji
@yashdhawade53417 ай бұрын
The best and clear explanation I've ever listened about Gradient Descent. Keep up the good work!🙌
@RanjiRaj187 ай бұрын
Awesome, thank you!
@aryandeshpande12412 жыл бұрын
this might be the most underrated explanation on youtube
@rajapal97363 жыл бұрын
Hi, Your video is helpful for beginners to understand the concept. One suggestion: In the very beginning of the video when you write the equation of your predicted line remember to mark it as y(cap) = mx(i) + c. It is not y(i) which is the actual data point.
@amarnammilton9 ай бұрын
its a very good description, The way you teach is humble and appreciatable.
@RanjiRaj189 ай бұрын
Thank you very much!
@kvv66719 ай бұрын
When my ML teacher teaching this , I felt I am learning some rocket science ,but you are teaching it felt very easy , thank you Sir😊
@RanjiRaj189 ай бұрын
Glad to hear that
@chinmaysrivastava32125 ай бұрын
I was unable to understand this topic tried many videos but this was the most useful video thankss
@vedanthbaliga76863 жыл бұрын
This is what I pay my internet bill for! Thanks a lot!
@sumanmondal52764 жыл бұрын
Your hard work made the concept very easy to grasp. Kudos.......
@Sagar_Tachtode_7772 жыл бұрын
Everything is so easy on this channel, great work Man!
@RanjiRaj182 жыл бұрын
Glad you like them!
@swaroopthomare7237 Жыл бұрын
Hey thank you so much for this content since I started studying regression using your videos , I became huge fan of yours
@RanjiRaj18 Жыл бұрын
Thank you for your comment. Glad you like it ;)
@alhassanturay72338 ай бұрын
Truest your the best. You solve my long time machine learning challenge.
@manishjain9703 Жыл бұрын
I am a beginner and as a beginner I was struggling understand the gradient decent concept. I have seen many videos on gradient decent however all of them skipped explaining the derivative part however you explained it very well both (total and partial) with solution . Thanks!
@RanjiRaj18 Жыл бұрын
Great to hear!
@vishaldas63464 жыл бұрын
Man, you've won my heart, you kept it so simple, best way of explaining Gradient Descent. Can you please help me in using learning rate in the equation and number of steps used in gradient descent with an example.
@theysigankumar16713 жыл бұрын
Sir thank you very much, this has been so helpful since my course will only get tougher from here onwards and u helped me understand the basics
@maneetsaluja Жыл бұрын
Great explanation with an example. This is the way to explain such concepts.
@RanjiRaj18 Жыл бұрын
Thank you for the comment. Happy Learning!
@classy_Girl8920 Жыл бұрын
perfect video for core concept understanding , amazing.. I love the explanation.. thankyou so much
@tusharsub10002 жыл бұрын
As far as I know, gradient descent doesn't talk about solving 'm' and 'c' directly by putting them into two equations like the way you did it here..Because sometime the expression that we get after differentiation becomes so complex especially with logistic regression, random forest and other complex model in deep learning that solving 'm' and 'c' directly becomes extremely tricky and time complexity becomes very high...So Gradient descent talks about solving 'm' and 'c' by some trial and test method. Starting with some dummy value of 'm' and 'c' and putting those values in the equation of differentiation and check if the value of differentiation(say D0) for those 'm' and 'c' becomes 0 or close to 0. If not, then subtract that differentiation value(D0) from the old value of 'm' and 'c' and get a new 'm' and 'c' and again check if the value of differentiation (say D1) for those new 'm' and 'c' comes close to 0. Continue like this till you find that old 'm' - value of differentiation is very very close to 0. That 'm' becomes your actual 'm' and similarly the same thing to be done for 'c'.
@RanjiRaj182 жыл бұрын
This video was an intuitive way for understanding gradient descent for beginners. Anyways appreciate your time to quote about your understanding of GD.
@ArunKumar-yb2jn2 жыл бұрын
@@RanjiRaj18 Good explanation. I think you have shown derivation for Ordinary Least Squares method. As far as Machine Learning is concerned it has to be slightly adapted.
@maxpatrickoliviermorin2489 Жыл бұрын
He solved m and c in this case because it wasn't a very complex example. Only one independent variable. In a multiple linear regression it would have been much more complex.
@jayaprakashs44125 ай бұрын
Very good explanation. It would've been good if you could've explained the usage of learning rate usage to find a minimum point.
@sonnyarulanandam2 ай бұрын
Very good explanation of gradient descent
@shaun22013 жыл бұрын
Hi @ranjiraj , at 21:37 u have given a wrong explanation in the partial derivative w.r.t C... d/dC (-C) will be -1 ,then why are you treating it as constant whereas in d/dc mx should be taken as constant.
@BADURELGADIR-dd2ck2 ай бұрын
simple and useful lecture.. thanks
@RanjiRaj182 ай бұрын
You are welcome
@sundar814711 ай бұрын
Thanks for the clear explanation sir
@lingadevaruhp55765 ай бұрын
Really amazing, thank you so much sir, keep rocking
@RanjiRaj185 ай бұрын
Always welcome
@trendhindifacts4 ай бұрын
Well explained bro ❤ just bring another video for statistics and linear algebra 🎉
@bhamidimaharshi27 күн бұрын
explanation is simply awesome.....
@Rambabukatta-ox6tc4 ай бұрын
very nicely explained Bro
@RanjiRaj184 ай бұрын
Glad you liked it
@vigneshwar28976 ай бұрын
will the sign ( direction ) for calculating m and b at last change, from addition to subtraction if we take "y-pred - y" instead of "y - y-pred" lke you have done in cost function ? i saw at few articles where this was mentioned but it was not clear.
@MrAmarSindol2 жыл бұрын
killer explanation !! amazingly amazing !! thank you bro
@anushadevi49373 жыл бұрын
Thank you so much, I got a clear picture of the topic now.
@RanjiRaj183 жыл бұрын
You are welcome!
@vasachisenjubean59443 жыл бұрын
you earned a subscriber my man
@anarkaliprabhakar6640 Жыл бұрын
sir u explained so well
@Shivendra72772 жыл бұрын
Thankyou sir , Make more videos on machine learning concepts .
@Venomus6583 жыл бұрын
Thank you! Wish me luck on exam about it!
@RanjiRaj183 жыл бұрын
Good Luck 👍
@OpeLeke2 жыл бұрын
excellent video
@RajSingh-ik3og2 ай бұрын
great explainnation
@aaryan34612 жыл бұрын
Great video man. Loved it.
@manasagowrikottur82424 жыл бұрын
Thanks for this tutorial sir..made it very easy and simple.
@sherifbadawy81882 жыл бұрын
one of the best
@suhasrewatkar9001Ай бұрын
Best explanation sir
@salmansayyad45223 жыл бұрын
Thanks a lot sir! It was really helpful. Excellent explaination.
@pursuitofgrowthwithtr4 жыл бұрын
your videos are really nice, good content and presentation...keep it up sir.
@helloworld27402 жыл бұрын
really nice approch to teach thank you sirji
@danielsehnoutek20163 ай бұрын
Absolutely the best explanation
@danielsehnoutek20163 ай бұрын
If I got it your last example is analytical solution, but it couldn't been done everytime, then we use iterative solution with alpha learning rate?
@sanusimuhammad74669 ай бұрын
i have this video over and over again, it the most satisfying video i have seen, in as much as gradient decent is concer, but i have questions, 1 what happen to the 2 tha became the multiple of the function as chain rules implies, then what happen to the no in the cost function. i know its mean squared errored thing. in my small assumption either of the values cant be thrown away just like that mathematically. please help with explanation.
@1234wellwell3 жыл бұрын
Thanks so much for the video. It helped me a lot.
@patelraj31404 жыл бұрын
Thank you so much sir for such a perfect explanation....🙏🙏👏👏👏
@dheerajverma1892 ай бұрын
Sir linear regression is not used for classification as you said in satrting of video while explaining
@SrikantBhusan4 ай бұрын
between the timestamp 21:39 to 21:45 you told that partial derivative of y(i)-mx-c with respect to c is 0 so only take minus sign which is wrong it will be -1 because of here c is not constant.
@shivammodi11053 жыл бұрын
Lovely explanation
@sarasijbasumallick40362 жыл бұрын
can you please tell me why the curve is much sharp when you draw the graph with respect to j and c ? please tell
@user-dd7el9dp3dАй бұрын
At 20.37, you discarded n/2. I would like to know why?
@mahmoodapurbo55377 ай бұрын
Thanks bro.
@umermehboob56303 жыл бұрын
That is a explanation. I have one question, where would the learning rate be actually used in computation. Like in your numerical example. We found the outputs and calculated corresponding m and c. How does the learning rate is catered. Secondly, when we multiply learning rate with derivative, what does it gives us ?
@nileshpandey57242 жыл бұрын
thank you so much sir
@debrajnath60313 жыл бұрын
The explanation of mathematical formula is absolutely fantastic. The explanation was about with single feature. But if we have multiple feature, what to be changed in the equation? Can you please let us know that. Thanks very much, and we will love to see this kind of videos shortly.
@RanjiRaj183 жыл бұрын
In case of multiple features or weights we have to conisder them individually by taking the partial derivtaive. This video is just a general idea of gradient descent. Hope it answers your question.
@Nudaykumar4 жыл бұрын
Hi one question here: First derivative: xsquare =2x Second derivative = 2 ( replaced in same location) Third derivative = 0 While applying same in mean square error formula First derivative= I understood square to 2/n(•••) Second derivative: with respective to slope It should be 2/n I=1 to n (yi -xi -c) here I applied second derivative replacing. Since -mxi converts to -xi. But In ur explanation instead of replacing, you brought second derivative to starting as below: 2/n I= 1 to n -xi(yi-mxi-c) In the same way for intercept. One more At the end , what happened to 2/n? Please correct me if I am wrong.
@RanjiRaj184 жыл бұрын
2/n Σi=1 to n -xi(yi-mxi-c) this comes from chain rule watch this part carefully again in the video, this (y-mxi-c) is the result of (yi-mxi-c)^2 and now since we want to differentiate wrt to slope m again you take the derivative now you treat the y and c as constants and what's left is -mxi so you get -xi that's what you get and you multiply with this(y-mxi-c). 2/n is a constant say for any number, n=5; 2/5 =constant you eventually equate it to zero so it vanishes away. Hope now you understand!
@mihirnaik33832 жыл бұрын
Thanks Buddy :)
@praveenkumar-nh5qs4 жыл бұрын
Nicely explained.
@RahulTiwari-oe1ww2 жыл бұрын
Well explained
@testenma51554 жыл бұрын
Hi Ranji Sir, i have a doubt. In 17:00 you have mentioned that we need to take derivative because we have two variables. And mentioned variables as x and c. But I think you were supposed to say m and c. later in 17:27 you mentioning about two parameters m and c. Please verify whether it correct or not. If i have pointed out wrong please apologize me.
@RanjiRaj184 жыл бұрын
Yes you are correct we have to take derivative wrt m and c
@ramnarayan33233 жыл бұрын
Thanks ...very well explained
@nightsky5037 Жыл бұрын
why do we set the derivative equal to 0? i mean the gradient at the minima might not be equal to zero for all curves
@mayank265memories3 жыл бұрын
Amazing lecture. x^n, will not have a 3rd order derivate to be 0, it will be n+1 order derivate.
@ayushsingh-qn8sb3 жыл бұрын
great explaination
@aerogrampur3 жыл бұрын
keep up the good work !
@mariawilson68074 жыл бұрын
Sir my maths is quite weak i want to start my career in data science i know that i can make my math strong but how should i start to learn maths for data science
@RanjiRaj184 жыл бұрын
Hello Maria, you can refer to websites like www.mathsisfun.com/ to learn the basics. Hope it helps!
@mariawilson68074 жыл бұрын
@@RanjiRaj18 thanks sir
@mariawilson68074 жыл бұрын
@@RanjiRaj18 Sir its very low level mathematics I am in sybsc it
@dineshlogu93683 жыл бұрын
can you please explain me why we are squaring the step at 4.36 . everything is clear to me except this one squaring step I cant able to understand..
@suryakrishna760 Жыл бұрын
What should i do if i am to apply learning rate of some value?
@fpl86483 жыл бұрын
thank you!!!
@fpl86483 жыл бұрын
It was very helpful, in writing a thesis. Could you also indicate some bibliography for citations
@chaithanyack3 ай бұрын
What type is it batch gradient?
@aqharinasrin7002 Жыл бұрын
dear sir, I still cannot connected what is the purpose we do m = m - lambda * dJ/dm and c = c - lambda * dJ/dC
@leninfonseca7129 Жыл бұрын
Yess .. exactly....plz explain the proof of these 2 equations
@AdityaSingh-lf7oe4 жыл бұрын
Hi Ranji sir, I wanted to ask that if there are if our line is of the form M1*(feature1) + M2*(feature2).... Mn*(feature n) + c, do we have to follow same steps and calculate dJ/dm for all M1, M2...Mn?
@RanjiRaj184 жыл бұрын
Yes, one by one
@rameshthamizhselvan24584 жыл бұрын
Excellent...
@apoorva36352 жыл бұрын
Why do we need partial derivative when we have the total derivative?
@RanjiRaj182 жыл бұрын
When there are relatively larger coefficients in your model, taking total derivate would be a diffuícult task and also to estimate the optimal parameter. Partial derivatives reduces the workload by keeping one parameter as constant and determine the other.
@iramarshad7003 жыл бұрын
So gradient descent is our cost function to calculate the error
@laodrofotic77133 жыл бұрын
J = 1/(2*m) * sum (h(x)-y)^2. being h(x) the hipotesis and y the accurate value... at 3:37 you got them mixed up right? damn man.. no wonder people get confused
@pavankumar86732 жыл бұрын
Linear regression for classfication at 0:20???
@dineshlogu93683 жыл бұрын
Thanks you so much but I have small clarification regarding differentiate. why we are differentiate with respect to m , c & why we should not differentiate with respect to x to find out the value of y..
@RanjiRaj183 жыл бұрын
Because m, c are the weights that we want to determine which will give the best equation for curve fitting.
@dineshlogu93683 жыл бұрын
@@RanjiRaj18 thanks you so much for spending time to respond to my comment..
@sudhansumtripathy Жыл бұрын
hi, sir do you have the python code using tensor flow or do you have any recordings of ML using TF
@OpeLeke2 жыл бұрын
can this method work for an equation with multiple slopes?
@testenma51554 жыл бұрын
How did the 2/n gone from equation when dJ/dm and dj/dc was assigbed to 0
@RanjiRaj184 жыл бұрын
2/n is a constant say you take n =5 so it becomes 2/5 so it's derivative is zero
@testenma51554 жыл бұрын
@@RanjiRaj18 Thank you Ranji
@sandhu6355 Жыл бұрын
bro please ans this question why we are taking summation of c in one equation and not in other ---> one is 5C why
@sandhu6355 Жыл бұрын
Plase ans
@sirajmotaung69303 жыл бұрын
Thank you so much..quick question, when/how do we use the learning rate in this regard?
@RanjiRaj183 жыл бұрын
If I understood your question correctly then: When? learning rate is made use for convergence, it should not be neither too large nor too low just optimal, so that your traning process is complete. How? You can use learning rate schedule or can use optimizers like ADAM.
@sirajmotaung69303 жыл бұрын
@@RanjiRaj18 Yes, Alright thank you so much. Your vid was really helpful.
@suryatej8392 жыл бұрын
is it a sweet, in the middle of a hyperplane?
@tonmoy24314 жыл бұрын
thank you sir
@mohamedelbatoty3 жыл бұрын
Thanks Bro :)
@viddeshk80202 жыл бұрын
🙂👍 nice like that
@abhiaaron1715 Жыл бұрын
why at the end multiply the 2 nd equation with 5
@RanjiRaj18 Жыл бұрын
To make equation balance on both sides for cancellation. Those are basic algebraic rules.
@divyamohan61134 жыл бұрын
thanks a lot..... :)
@mariawilson68074 жыл бұрын
Which level of maths is required 11th and 12th or degree level mathematics ?
@RanjiRaj184 жыл бұрын
Personally both Derivatives, differential equations, Matrices and vector concepts
@mariawilson68074 жыл бұрын
@@RanjiRaj18 thanks
@mohdhashim23214 жыл бұрын
sir explane krne ke baad thoda side ho jaya kijiye screenshot lena rehta hai
@shafiqahmad90573 жыл бұрын
Sir can you recommend a book for machine learning with mathematical background please
@RanjiRaj183 жыл бұрын
You can refer the book by `Tom Mitchell`
@shafiqahmad90573 жыл бұрын
@@RanjiRaj18 sir please what is book name and if you share pdf link it will be better
@RanjiRaj183 жыл бұрын
@@shafiqahmad9057 You can check on google it is open source
@shafiqahmad90573 жыл бұрын
@@RanjiRaj18 thank you for very fast respomse
@someshh72632 жыл бұрын
Sorry bro, Not clear what you saying..
@manashalder39994 жыл бұрын
havoc explanation
@mondal18392 жыл бұрын
Doesn't he look like Bharati's (a comedian on Kapil Sharma's show) husband?😅