Forward Propagation and Backward Propagation | Neural Networks | How to train Neural Networks

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let bro cook

Nicely explained. Keep up the good job!

wait you haven't explained backpropagation at all

Your videos are very helpful. It will be great if you sort the video..Thank you😇😇😇

great video

Fantastic explanation. Thank you

y no subtitles?

Literally best. Crisp and clear!! Thank you

Very helpful and to the point and correct!

Brother your explanation was great but there are some mistakes i have pointed out.

best explanation, best playlists
I don't usually interact with the algorithm much by giving likes and dropping comments or liking but you beat me into submission with this. Hopefully I understand the rest of it too lol.

You explain better than popular course instructor on deep learning

Best explanation I've seen so far

B1 and B2 are initialized randomly too ?

Absolutely loved the way you explain. So easy to understand. Thank you

Excellent explanation jazakallah bro

Please share code algorithm backpropagate

so glad I found this channel!!

Super sir. I have learned more information from this and also calculation way. It's very useful to our study. Thank you sir

Can A* actually be Z*, e.g. A1 = Z1?

I've always felt as if I was on the cusp of understanding neural nets but this video brought me past the hump and explained it perfectly! Thank you so much!

Sir it's W ¹¹[¹] * a⁰[1] right? You've done it as W ¹¹[¹] * a¹[1] at the matrix multiplication, can you just verify I'm wrong?

what is this B1

Such a simple and neat explanation.

This was actually pretty straight forward

Small doubt, what is f(z1)...I am assuming these are just different type of activation functions...where input is just the weight of current layer*input from previous layers...is that correct?

this video should be titled " Explain - Forward and Backward Propagation - to Me Like I'm Five. Thanks man you saved me a lot of time.

Good job. But Gradient descent W2 and W1 mus be updated simultaneously.

Amazing work, keep it going :)

Thanks man. The slides were amazingly put up.

Good Explanation !!

you explained in very clear and easy ways. Thank you, this is so helpful!

Isnt the equation : Z= W.X+B = transpose(W)*X + B.Hence the weight matrix what you have given is wrong right?

hi can you put caption option

Great video, and great explanation thanks dude!

Im a bit confuse through the exponent notations since some of it were not corresponding to the other

You are great. It will be very good if you continue.

Your videos on neural networks are really good. Can you please also upload videos for generalized neural networks too, that would really be helpful P.S Keep Up the good work!!!

Awesome, really helpful! Thank you

thank u sir it was really helpful

you are really awesome. love your teaching ability

hi, how to calculate the cost?

great video as always

great video,
Please also make a video on SVM as soon as possible