The Jacobian : Data Science Basics

Let's learn about the all-powerful Jacobian in data science!
My Patreon : www.patreon.com/user?u=49277905

[@oc1655]

i have a phd in machine learning, yet i regularly come revisit concepts (one i vividly remember is your description of kernel methods) you are covering here. thank you for your work

[@markbordelon1601]

You have a gift for calm, simple, sequential explanation. Thank you!

[@ritvikmath]

You're very welcome!

[@borispapic9510]

Just quickly popping in to say I really like your content so far. It helps me quickly recall some of these concepts I haven't used in a while and kind of understand again why things work how they work.

[@ritvikmath]

Thanks!

[@havehalkow]

I just looked at your old videos and this one, there's quite a step up in the way you explain.

[@denissobczyk9363]

What an incredible video! Your channel must go tenfold within the next year.

[@mahesh1234m]

Awesome explanation Rithvik. In addition to this can you please cover how autodiff works by making use of these jacobians.

[@ritvikmath]

Great suggestion!

[@achudakhinkudachin2048]

Something new learned! Thank you for your efforts, Ritvik!

[@ritvikmath]

of course!

[@clairemcglave7860]

This was incredibly helpful - thank you! This is the first time Jacobians have made a semblance of sense to me.

[@fabriai]

Straight forward explanation of the Jacobians. It helped me a lot. Thanks!

[@paingzinkyaw331]

Bro you make my life way more enjoyable and easier. Thank you so much

[@daclachoang368]

Clear and sort vid that is really helpful to recall some of the jacobian matrix use cases

[@mathcritic]

Great explanation delivered using plain language. Thanks!

[@odysy5179]

Amazing explanation. I was just looking for a good definition of a jacobian matrix, but came back with some new insights in ML. Thanks!

[@ritvikmath]

Glad it was helpful!

[@nihalyadalam8933]

Superb explanation! you explained the complicated topic in a simple way.

[@ResilientFighter]

As always, great videos man! :)

[@ritvikmath]

Thanks :)

[@Gibson-xn8xk]

thank you very much, simple and intuitive presentation of the material!

[@Nerdimo]

This was freaking awesome. Bravo!

[@zlucoblij]

Thanks for talking about the application. That made it very clear.

[@baronvonherzenberger2473]

Wow! Awesome video, right to the point, well explained. Thanks a lot!

[@user-lo5tl5wf1s]

appreciated the content and the way you are teaching!

[@tinkeringengr]

Really nice explanation. Well done!

[@Waqarahmad-xd9kt]

Excellent explaination. Thanks

[@kyosukefukumoto9382]

Great Video! Thank you so much!

[@orca719]

Oh my god! This is the absolute best explaination about Jacobian matrix Thank you so much!

[@jasonj7285]

Nicely done bro! got a lot of help!

[@Life_on_wheeel]

Thanks for such a simplified explanation 👍👍

[@ritvikmath]

My pleasure

[@marathonour]

This is awesome explanation, thanks!

[@siddhantrai7529]

Damn!! That was really cool. Thank you for such a wonderful explaination.

[@ritvikmath]

No problem!

[@nitishjawahar4186]

this is exactly what i was looking for, thank you!!

[@rahulahuja1412]

Great explanation. Good topic.

[@pontosinterligados]

Thank you! Even though I use the jacobian matrix in pose estimation from image, I feel like the more the ways to look at it, the better.

[@preranabora]

very clear explanation thank you

[@sanjeetsingh4781]

Really nice explanation.

[@user-or7ji5hv8y]

great explanation and great example.

[@ritvikmath]

Thanks!!

[@LucasDimoveo]

This made the Jacobian way less intimidating. Thank you!

[@ritvikmath]

Great to hear!

[@JeyongShin]

Thanks for clear explanation. :)

[@alexanderehrlich9754]

This was a very good explanation, thanks you!

[@AnimeshSharma1977]

Very well done Ritvik, thanks for sharing! Could be nice to throw in the calculus "chain rule" for jacobian when you talk about their multiplication? And why just stop there, could be even more cool to hear the story of higher order derivatives!! Looking forwards :)

[@ritvikmath]

Great suggestion!

[@SaschaRobitzki]

This is a really good video!

[@houyao2147]

Very impressive!

[@faustovrz]

Thank you!

[@2207amol]

Dear ritvik I am studying 6 axis robot control and your videos immensely help me understand what all this stuff means. Thanks a lot for all the effort you put into these videos...

[@ritvikmath]

Glad to hear that

[@tlgysr]

You are just awesome!

[@daasdaham6964]

Thanks man, the other way to calculate the derivative w.r.t. input was always non-intuitive for me. This is so easy to understand...

[@ritvikmath]

I'm glad!

[@abdulelahaljeffery6234]

Great video 👍 just a few questions I had while watching: 1- what is the meaning of a high jacobian? 2- is there some boundries to jacobian's values? 3- is it always a scaler value?

[@veganath]

Thanks so good to get an insight(my head still hurts) into how Jacobians relate to ML.. even if it is without the activation function.... fair play.

[@djfl58mdlwqlf]

awesome vid always

[@ritvikmath]

Thanks!

[@jacklam5658]

It seems Jacobian has a lot of different implications. I learn it by myself by watching diffenent utube videos. Your video is one of the best I come across. If you can upload more videos about the different underneath meanings and applications of Jacobian would be very great ! Thanks a lot ! Jack Lam from Hong Kong

[@ritvikmath]

Glad it was helpful!

[@kiannaderi8374]

thank you

[@danielwiczew]

Very goood

[@princewillinyang5993]

This video put me out of my misery. Bless you sir.

[@thatsharma1066]

thanks

[@leonardomiguel1943]

So clear. Congrats for didactic.

[@ebadrahman1832]

best explanation in the web

[@gaofan2856]

Damn, I learnt about Jacobian without even knowing the "when" and "why" to use it

[@kina4288]

Don't know why we cannot use actual values to represent the flow of the steps. Mathematics are not abstract until we try to generalise and use all these subscripts , Understand the effort you guys put into these video. Hope my suggestion can improve the understanding. Thank you

[@AarjanmaniKandel]

this is gold

[@ritvikmath]

🎉

[@neurojedi42]

Can you also make a video about hessian about ? Thank you

[@ritvikmath]

great suggestion!

[@ajithshenoy5566]

Hessian next? 🥺

[@ritvikmath]

Good suggestion!

[@86jam184]

Excellent presentation. My only request is to slow down and/or pause more. It takes my tiny brain a second to process each step. Thanks so much!

[@ritvikmath]

Thanks for the tip!

[@craighalpin1917]

Will this work when an activation function is included? How will that look? And is it a preferred way of dealing with it?

[@almasaabdulla8259]

love u

[@abokaled94]

super

[@meguellatiyounes8659]

Jacobians are used in gauss newton method

[@pratik6447]

In the second and third layer we are taking derivatives wrt to a1 and a2 and not further breaking down a1 and a2?

[@nativesun7661]

👏🏾👏🏾👏🏾👏🏾👏🏾👏🏾👏🏾👏🏾👏🏾

[@TymoteuszCejrowski93]

How does it relate to the backpropagation algorithm? Maybe it is worth mentioning briefly? Thank you!

[@ritvikmath]

great suggestion and thanks!

[@AsBi1]

good

[@Unremarkabler]

Hey I got confused at the very simple beginning... why f(x)=x^2 conducts to another df(x)/dx= 2dx? does it make sense? sorry if I am stupid mistake something

[@ireoluwaTH]

And my Mathematese keeps getting neater...

[@baqirhusain5652]

You are a math prophet sent by God to guide us ..

[@raptor4133]

The goal is to optimize the weights of the network. So why are you taking partial derivatives with respect to x instead of w?

[@nuclearcornflakes3542]

w vid

[@menoone2042]

Thank you sir, are you Indian ?

[@kulbhushansingh1101]

thanks

[@ritvikmath]

You're welcome!