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Spent the past weeks trying to grasp backpropagation mathematically. Spent countless days on videos and textbooks. This is by far the most intuitive explanation. Just subbed. Thank you.

Your explanation always stands first

will it work if I run a .py file with normal tensorflow written on windows?

what is need for installing Xcode ? can't we install pytorch without that ?

24:10 y.backward(torch.ones(3)) the input to y.backward is upstream gradient and the size of the upstream gradient should be same as the size of y and in previous cases we dont need insert upstream gradient cause y,backward() same as y.backward(torch.tensor([1]))

it doesn't works pip install tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal

I see this mps available, but still get CUDA error AssertionError: Torch not compiled with CUDA enabled

Which book are you following?

It’s almost impossible to listen to you, it sounds like your mouth is full of marbles

how model(x) work , is he should right model.forward(x)?????????

I want to make a note on the use of `super` method in the ``__init__`` method of the subclass. There's an alternative, less cumbersome syntax to use `super`: you can use `super().__init__()` instead of `super(NeuralNetwork, self).__init__()` (see timestamp 17:16). These expressions are both equivalent.

A really great thing about your tutorials is that you choose to explain on *simple* examples that has enough complexity to teach the concept, but simple enough so we can really grasp the concept. Explaining a concept on simple (enough) example that still has the essential characteristics of larger networks is key to understanding (in my opinion)! Thank you!

In my opinion, what makes this a great video is its (quick) review of the prerequisite knowledge, and including simple examples to that we can compute by hand and then verify that we actually get the same thing when using PyTorch. Thanks a lot!

Very informative , Thanks <3

thank you so much bro.... after so many trial error method i was able to install . thanks a lot man

Thanks Sir.

num_inputs and loss() are both undefined in your example code

For some reason I can only see and install pytorch 2.2.2 as latest. I am on same python version, Sonoma 14.5, M3 24gb.

nice video, birader aksanınızdan anladım

Great video with clear explanation! Thank you very much!

how about for pytorch?

the best course so far , thanks man

What does "measure the agreement of the assignments" mean ? agreeement sounds like similarity ? Can we say the larger NMI is, the more similar between the two assignments ?

thanks man i was finding it extremely difficult to understand the maths behind backward and detach (although i have doen it in my high school) because no one was explaining them in this depth.........love you 😍😍

Great video

HI there, very happy with this channel could you explain a bit simpler what is pipeline part!

Thank you sir

Why are all arrays converted to column matrices while applying sklearn

thnks myfriend - in last section - whats is the z.sum ? what is the the (SUM) function for ? why yyou put the sum ?

I read somewhere else that the normalized laplacian is Lnorm = D^(-1/2) L D^(-1/2) with L = D - W can you clarify why it is different here in your explanation please, thanks for you videos

This is the best explanation over whole YT.

why are you taking anisotropic distribution and taking the dot product in the beginning ?

These videos are so well made and offer an exhaustive and detailled explanation. I wish there would be more!

TYSM :)

You're the goat

Thank you for making this video!

What is phi with a1? Is it activation function.?

Thanks for sharing :) would be grate if there's a time stamp :(

Do i need to understand all this to use neural networks? I'm scared shitless but my curiculuum says i have to learn it

I couldn't really understand what a computational graph was until I watched this video. Thank you very much.

Why did we not use the tape.watch function while implementing the linear regression via neural net?

Great explanation, many thanks!

Great work!!! Many thanks to you, man. The whole RUclips couldn't give me such comprehensive course as yours!

very good thanks

Thank you🎉

thank you very much

Thank you for your video. It would be perfect if you could point to the places that you are explaining. When I am watching the video, I can't understand what part of the slide you are talking about.

Incredible video. Thank you for sharing this information for free!

Very helpful

Very helpful