04 PyTorch tutorial - How do computational graphs and autograd in PyTorch work

In this tutorial, we have talked about how the autograd system in PyTorch works and about its benefits. We also did a rewind of how the forward and backward pass work, and also how to calculate gradients.
If you found this video helpful, please drop a like and subscribe. If you have any questions, write them in the comment section below.
💻 Blog: datahacker.rs/004-computationa...
🎞 UA-cam: / @datahackerrs
🎞 Link for google collab script: colab.research.google.com/git...
.
.
.
Timestamps
00:00 Introduction
00:52 Computational graph forward pass
01:54 Python forward pass
02:25 Gradient calculation
02:49 Python backward pass
03:29 Turn off gradient calculation
04:58 Gradient accumulation
07:01 Chain rule
08:17 Derivative calculation visualized
10:15 .backward() function
.
.
.
.
#machinelearning #artificialintelligence #ai #datascience #python #deeplearning #technology #programming #coding #bigdata #computerscience #data #dataanalytics #tech #datascientist #iot #pythonprogramming #programmer #ml #developer #software #robotics #java #innovation #coder #javascript #datavisualization #analytics #neuralnetworks #bhfyp

[@josecuevas5814]

Thank you so much for the video! A practical, worry free, and clear explanation.

[@donfeto7636]

Awesome Video, would like to see more in-depth like this I like you don't ignore math, please do it with the loss functions of any algorithm you like (better if it is related to deep learning or GAN, diffusion model ..)

[@cristianarteaga]

Thank you for such a great explanation!

[@datahackerrs]

No problem !

[@xflory26x]

Can you please elaborate on what you are talking about in the backward() function step? It's not very clear what is happening when you reset the gradients with z.backward(v)

[@ashilshah3376]

Very nice explanation thank you

[@harshpandya9878]

great!!

[@AlexeyMatushevsky]

Very nice viedeo! Thank you !

[@datahackerrs]

Thanks for the nice comment, glad you like it!

[@user-ig3rp7fk9c]

What about matrices, how do we handle the backward computations (derivatives) in matrices. Can you share any article or something. Thanks.

[@bhavyabalan6225]

Hi, great video and explanation. In the last part why we need to define the vector of ones and why we do the division by length of the vector? The explanation for that change in code is not clear for me

[@yusun5722]

The reason it needs a vector is because the gradients are calculated using the Jacobian matrix. A vector must be right multiplied with it to get the resultant gradients. Detail: pytorch.org/tutorials/beginner/introyt/autogradyt_tutorial.html
For your second question, I think it's right NOT to divide by the length. If you manually calculate the derivative of dz/dx, it will be 20, 28 and 36.