PyTorch Functions and Tensor Operations

Please reply to this thread with the link to your blog post or Jupyter notebook.

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Hi everyone,
first of all thanks @aakashns for the time and effort to create this course!

For those interested in understanding a little bit more some of the most famous torch functions, here is my first assignment!
The notebook covers the basics of view, reshape, permute, flatten and cat, why they are useful and what are the main differences!

Any feedback would be greatly appreciated, here is the link!

2 Likes

Hi folks,
I am so glad to be in this course, my best thanks to @aakashns for so much time and effort spent, yet offering this course for free. I am wowed!
folks, I just completed my first assignment in the Pytorch module and I explained five interesting torch functions, they include:

  1. chunk
  2. transpose
  3. rand
  4. add
  5. inverse
    I would be glad to receive your feedback, suggestions and even corrections too, enjoy the rest of the course!. Zero to Gans!
    link to the assignment: click here
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nice work! @jacopo-repossi, truly, the functions view, reshape, permute and even flatten are quite too similar in a way, you were able to safely guide me through the difference, good job and well done.

1 Like

Hi Guys,

Please find the link to my notebook of our first assignment.
link - https://jovian.ai/hargurjeet/pytorch-functions-for-machine-learning

Thanks akansh, Appreciate your effort on the course and providing it us for free.

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Thank you to @aakashns for the interesting course and to jovian for this cool cloud infrastructure!
Good to see other guys sharing their assignments here, I find them useful and I hope to complete my own asap…

Edit
Here it is now!

Hey guys,
Here is my notebook link to our first assignment

Thanks to @aakashns for creating such a great opportunity and hats off to all his efforts. :clap::clap::clap:

Hi everyone,
thanks to @aakashns for this very interesting course.
Here is my notebook about the first lesson.


Impressed by the job of you all, very motivating that I am not alone to learn.

Assignment 1 completed !!!

What will be the deadline for the first assignment on PyTorch Functions and Tensor Operations?
Bcoz I’ll be a little bit busy in these 3 days. So let me know soon.

Hey everyone! Hope you’re all safe. :innocent: Here’s the link to my first pytorch assignment - https://jovian.ai/lavsdio2019/01-tensor-operations.

Your comments are greatly appreciated :v:t2: :blush:

Thanks @aakashns and Jovian team for your efforts! :clap:t2:

can someone pls explain about the below error


@aakashns

1 Like

can someone tell about torch.sparse_coo_tensor function. i already read the explanation in the given reference but unable to understand.
@aakashns

Hello everyone, and thanks to @aakashns for this great course.
Here is my first assignment on tensor-operations - https://jovian.ai/kunalsb/01-tensor-operations

Hello, here is my notebook on our first assignment based on “PyTorch Functions” - https://jovian.ai/sathi-satb/01-tensor-operations :innocent:

1 Like

Hello everyone, here is my assignment about functions that can take Boolean tensors as input.

Hi all,
Here is a list of Pytorch functions on tensors

Hi everyone,

Thanks to @@aakashns and jovian team to provide such opportunity to enhance our knowledge.
I just completed my first assignment in the Pytorch module and I explained five interesting torch functions, they include:

  • Pytorch masked_select() function
  • PyTorch fmod() function
  • pytorch sin() function
  • Pytorch torch.eye() function
  • Pytorch cat() function

here is the link of my notebookhttps://jovian.ai/parveenrohilla06/assignment-1-pytorch-and-its-functions:

Deep Learning with Pytorch Assignment 1

Your g is a multi-dimensional tensor, which will thus throw this error.
During lesson 1, the mse loss was a scalar, not a multi-dimensional tensor, that’s why the loss.backward() was giving no problem in the computations.

If you need to calculate the gradiend for a multi-dimensional tensor, you could pass a gradient with the same shape as g :

g.backward(torch.ones_like(g))

# Then display the gradients
print(d.grad)
print(f.grad)