Application of Calculus in Backpropagation

[@Orblitz]

Something I find crazy is that googles networks identified my video as having two main chapters, and MARKED THEM ACCORDINGLY.

[@KaRim-fc1sd]

Always a random small yt channel has the best learning videos

[@AWIraq]

there is always one guy that will give you what you need in 15 min . after you searched the KZbin for 5 Hours. ( and found no thing )

[@hahhey1372]

Don’t click on random video kids, or else you’ll learn about neural networks and calculus. Very scary.

[@bluedragontoybash2463]

LOL

[@mohammadmustafa2k19]

😂

[@kennethcarvalho3684]

This is truly excellent video i finally understand something after going through numerous videos

[@ricardoromao9132]

Watched some videos on backpropagation and yours is the most clear. Thanks

[@marcsaintjour3384]

Super great video. You are great at teaching. I hope you are a professor. Thank you, best explanation by far.

[@121Pal]

....this is a fantastic video...u really explained this well...

[@idk0075]

Thank you so much for making this, I was already familiar with backprop butt here were too many doubts of the inner working but this video made it clear. Again thank you!

[@mpbasics8285]

The best out here

[@Jonathan-ru9zl]

In 6:15, From the equation perspective, what is the value of y if there is a dog in the image?

[@okj4521]

correct me if I am wrong, but this is how I would explain it: y is not the actual image of a dog. It's a scalar, meaning "just" a "normal" number or value. it serves as an indicator. The image of a dog is nothing more but a matrix and this matrix contains certain numbers (or vectors). And the image of a dog can only be categorized as such if you also have images labled "not dog" to train the algorithm. in the end y and y' are nothing but abstract values to determine a difference between what the model has learned about the image of a dog and what an actual image of a dog looks like. if the difference between y and y' is relatively small, then this means that the algorithm is trained well for this case and might recognize images of a dog in the future.

[@alonsodehermes2885]

it's amazing how simple you made it sound. can you do a version for networks with multiple hidden layers please ?

[@enigmaticerror]

Thank you for such a great explanation!

[@Bbdu75yg]

Nice!

[@michaelbourne7821]

Have made a mistake in equations out h1 & out h2??

[@ccamp3175]

This is a superb video, EXCEPT that I think there's a mistake in the very first calculation. Shouldn't the sums be i1*w1 + i2*w2 and i1*w3 + i2*w4 ?? Please double check this; forgive me if I'm wrong.

[@Jonathan-ru9zl]

Me too Is it a mistake?

[@HAScrashedOfficial]

It's more like (net)h1 = i1*w1 + i2*w3 (net)h2 = i1*w2 + i2*w4

[@chandrakaran1]

@Orblitz , These slides are amazing. Can I have the link of this slide please. Want to print it out and keep for revision time to time.

[@ege1217]

that is an art

[@mohammadmustafa2k19]

Netflix level Content

[@tremaineification]

Why can't you just do the chain rule?

[@akashp01]

fix your title: What is calculios, backpropagiation ?

[@Orblitz]

Thanks lol, Im not the brightest at english