Chain Rule | Deep Learning Tutorial 15 (Tensorflow2.0, Keras & Python)

This video gives a very simple explanation of a chain rule that is used while training a neural network. Chain rule is something that is covered when you study differential calculus. Now don't worry about not knowing calculus, the chain rule is rather a simple mathematical concept. As a prerequisite of this video please watch my video on derivative in this sample deep learning series (link of entire series is below). It is even better if you watch this entire series step by step so that you have your foundations clear when you are watching this video.

🔖 Hashtags 🔖
#chainruleneuralnetwork #chainrule #chainrulepython #neuralnetworkpythontutorial #chainrulederivatives

Prerequisites for this series:   

[Ankurkumar]

Thanks for maintaining great pace of releasing new videos on this series....u r really a big help to lots of students and professionals around the globe. :)

[RichardBronosky]

When I was in school, I was never able to maintain interest in this long enough to grasp it. But now that I am doing robotics and have a need to reconcile the error between the Computer Vision prediction and physical coordinates, this is extremely interesting, and therefore easy to learn.

Put another way, my steps are this:
* Pull an image from a camera
* Select a point (x, y) on the image to move finger to
* Convert that to a point in space (x, y, z)
* Move finger
* Pull image from camera
* Locate the point (x, y) on the image where the finger appears
* Correct the error

Well that simple sounding last step is where the process in your video applies.

[SatyaBhambhani]

Man, this series! I love it. As an Econometrician, I can see how well he connects even the basics like derivatives to the depths of gradient descent.

[yogeshbharadwaj]

Never thought Chain Rule concept is so simple.... now I'm getting confidence in Maths day by day....its all because of you sir....you are proving that "Way of Teaching" matters a lot in making students/any person understand the Off !!

[satishsangole]

Precise, Concrete, Complete, and Simple. You are simply amazing!

[sooryaprakash]

Best Video with a simple and clear explanation I have ever seen!.Will share with all my ml peeps!

[sejalanand]

As simple as it could get. Thanks a lot!!! You have a gift, sir.

[rndtest]

Code Basics : your explanation is THE BEST ... You make very complex things very simple, easy to understand

[vivekmodi]

Your explanation is too easy to understand. Your tutorials make the complex topic to simple. Thank you. You're great teacher for AI and machine learning.

[chandralekhadhanasekaran]

Each time I see your videos, I am learning new things and getting excited. Keep Exciting us :)

[karthikc]

Sir
Looking great today

Hope this continues 😊

[vishaljaiswar]

you are such a good teacher! those complex concept I never get understand them before watching your videos

[RajaKumar-hgwl]

Hats off to you!!! Very superb. I have gone through different courses / learnt from different places. But the concept of Chain Rule is not explained this much simpler way in other places. Thank you so much.

[aakuthotaharibabu]

I fell thankful to find a teacher like u

[yashmaheshwari]

for all those who are asking why squaring error ?
lets say your model output [0, 1] for two inputs and actual output should be [1, 0]
calculate error without squaring or doing absolute :
total error = ((actual first output - predicted 1)+(actual second output - predicted 2))/2
total error = ((0-1) +(1-0))/2
total error = (-1 + 1)/2
total error = 0/2 =0
what ?
error is 0 our model is perfect ?

squaring error this time :
total error = ((0-1)^2 +(1-0)^2)/2
total error = (1+1)/2=1

[hugomartinez]

thanks a lot for the explanation, now I finally understand this :)

[rushikeshdeshmukh]

Excellent video lecture and this entire series is superb. Thanks a lot for these methodical, step by step systematic videos.

I just would like to provide constructive feedback for the chain rule formula mentioned by you after 12:00 minutes in this video. There are two paths from A to Z one via X and another via Y. So the partial derivative of Z with respect to A should consider these two paths.

[harshsawant]

You are a great teacher ... Hats off to you 🙌🙌

[ramsunil]

Thanks for explaining in a simple way .now the difficult puzzle has been solved for me.

[vijaya]

Thankyou for such a great explanation sir, I have seen all your ML and python pandas and numpy series,
All were awesome. Hope you continue with same pace!!