Derivative of Cost function for Logistic Regression | Machine Learning

We will compute the Derivative of Cost Function for Logistic Regression. While implementing Gradient Descent algorithm in Machine learning, we need to use Derivative of Cost Function. Computing it, can be difficult if you are new to Derivative and Calculus. But going step by step, we can simply compute Derivative of Cost Function for Logistic Regression.

It will help us minimizing the Logistic Regression Cost Function, and thus improving our model accuracy.

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This is Your Lane to Machine Learning ⭐

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[CodingLane]

If you found this video valuable, then hit the like button👍, and don't forget to subscribe ▶ to my channel as I upload a new Machine Learning Tutorial every week.

[mikechen]

There are not many videos explaining how the gradient is derived through partial derivatives.. Thank you very much for this treasured video!

[sx.]

probably the greatest explanation ever, making it so easy to understand. Thank you bro! We need more people like you

[vikramyadav-fevj]

A heartfull thankx for u 😭😭 i was gone mad for this ...but finally u were my god @ last thank you ❤️

[JokerJax]

thanks buddy that was really helpful. i understood the concept in just 10 minutes!

[sadaananthanbucheliyan]

Wow, I really appreciate your effort in explaining things clearly. I thank you for explaining this concept.

[empowercode]

Hey! I just found your channel and subscribed, love what you're doing!

I appreciate how clear and detailed your explanations are as well as the depth of knowledge you have surrounding the topic! Since I run a tech education channel as well, I love to see fellow Content Creators sharing, educating, and inspiring a large global audience. I wish you the best of luck on your YouTube Journey, can't wait to see you succeed! Your content really stands out and you've put so much thought into your videos!
Cheers, happy holidays, and keep up the great work!

[타케시-gw]

Finally I understood how it works!! Thank you soo much!

[amadlover]

Nice one. Towards the end of the derivation of dcost/dw, and dcost/db.... do we need to take the mean of the values?

[ragook]

Nice Video, where can i find derivative functions video? could you please guide me?

[thejohnnybhai]

Thanks so much. Have a small query, isn't derivative of logx = 1/xln(10) ?

[shonendumm]

Thanks for breaking down the math proof. Awesome work.

[VatsalaNundloll]

as always, very clear and concise explanation. Thank you so much

[hayki_ds]

Nice, man. Explained step by step. Good job.

[rahulvansh]

liked your clear explanation. Loved quickly rubbing the board, saves time. A suggestion: Further you can add how you converted individual element wise to vectorized form. Thank you.

[mukulsharma]

The minus sign is applied to whole equation, i.e. - Sum( y log(a) + (1-y)log(1-a). Thus if we take its derivative w.r.t. a we will get -[ y/a + (1-y)/(1-a)]. Where as you have taken -y/a + (1-y)/(1-a). Can you please comment on this?

[dharshansagargangadharan]

At 5:13, why did you rub T on the W? Can you please explain that? And, I think instead of writing W(Transpose)X you could write as X(transpose)W for easy differentiability. (Both are same)

[thongonk]

This is so great Thank you so much for explaining.

[mohammadferdosian]

it's amazing. thank you so much. You teach these fairly hard concepts very well and clearly.🤩
the sound of the video is a little weak. and I should use hands-free to understand clearly what you are saying. I think it will be much better if you record your videos (not only this one) with a higher sound and also better quality. 👍👍

[srihitharavu]

Hi Jay.. kudos for making informative videos..could you make a video explaining the derivative of tanh activation function