Backpropagation For Neural Networks Explained | Deep Learning Tutorial

[@sabre_code]

Probably the best explanation to Backpropagation on YT. Thanks. :)

[@KS-df1cp]

Nice!!! This is very helpful, especially in interviews. I am so sorry to see that there aren't many comments and views here but hope this video picks up. Thank you again!

[@user-ow8ko4lh3l]

You described this concept amazingly.

[@abdelrhmanabouelfoutouh4150]

Absolute fantastic explaination

[@mrrank]

Added to my playlist on AI top videos

[@luis96xd]

Amazing video, it was wekk explained, thanks!

[@djsosbxbdirndxnkcbebxhxbe]

Don’t listen to these people who tell you that you content is awesome. It’s poor and not fulfilled with a lot of necessary information

[@KS-df1cp]

At 7.28 why did the equation not contain ds/dy-hat in chain rule multiplier? thanks

[@j7m7f]

Because we have it already included in d loss / d y hat which is explained in 6:45

[@KS-df1cp]

@@j7m7f Thank you :) I got the job! This is very helpful.

[@carrinka]

Thank you! That was very helpful.

[@user-bg8vk9fh6n]

Excellent tutorial!!

[@mahdiamrollahi8456]

Hello, just have a question: in dnn, sometimes we have a complicated estimated function at the end including many multiplication and applying activation functions. How do we know that is differentiable or not? We just add layers and changing activation fn without knowing it.

[@anirbansarkar6306]

Great video. Really helpful

[@narendrapratapsinghparmar91]

Thanks

[@shadeersadikeen1052]

Be simple 🙄you are confusing mann

[@JoselynArevalo]

I think this is as simple as it can get, he explains it really well and clearly

[@hansisbrucker813]

What do you not understand? If you are more specific then maybe people can help you 👀

[@nikhilverma4976]

I cant understand your handwriting and you should keep your language simple.