PyTorch Tutorial 07 - Linear Regression

New Tutorial series about Deep Learning with PyTorch!

In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far:

- Training Pipeline in PyTorch
- Model Design
- Loss and Optimizer
- Automatic Training steps with forward pass, backward pass, and weight updates

Part 07: Linear Regression

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Part 01:

Linear Regression from scratch:

Code for this tutorial series:

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#Python #DeepLearning #Pytorch

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[alexlang]

Dear Patrick
your lectures are awesome! What a great way to get a first grip on the subject without reading through difficult manuals or reading a big book. Fab!

[psy_duck]

I love your strong German accent. I study at a German university and you remind me of my professor. Thank you very much

[michaeltsang]

I've been binge watching your vids, and they have been helpful for me. I'm trying to get a software developer job, and I want to put pytorch on my belt. So, thanks for these vids again. Your information is straight to the point, accurate and easy to follow.

[soerengebbert]

Zuerst vielen Dank für deine ausgezeichneten Tutorials. Jedoch macht es didaktisch viel Sinn, die Zwischenergebnisse zu zeigen, um dem Zuseher ein tieferes Verständnis zu vermitteln.

[davidkhassias]

All your series are great! Thanks a lot!

[naveedmazhar]

All his lectures are so good. I really liked his work

[mohammadkarimi]

Thank you very much.
I love your gernan accent, and your tutorial helped me learn pytorch.

[swethanandyala]

Great Job sir! Thank you so much for your informative sessions

[ridael-mehdawe]

thank you so much, so detailed lecture, appreciated

[computerscience]

thank you so much for giving a complex concept in an easy way I am learning pytorch from your tutorial. please extend to seq2seq model and also make example of language translation in RNN module thank you again

[jyotipch]

Question: At 100th epoch, the loss is 567. Without looking at the plot, how do I know that this loss is good enough? Because in the previous examples, the losses were near zero.

[bijjalanaganithin]

Thank You for the excellent tutorial series
I am new to PyTorch and I am a little confused at 6:07 here MSELoss is a class so criterion will be an object but you said it as a callable function and used this object as a function to compute the loss. how is it possible to use the object of a class as a function? can u please explain or point out to some resources
Thank You

[ravivarma]

Hi Bro,
I couldn't stop thanking you again and again...It was such an amazing explanation.
can we connect in linked-In or in any other platforms aswell?

[Aditya_Kumar__pass]

okay so a quick question.
when i use pytorch. i get very high accuracy in my new data.
but when i use sklearn, though i get very high accuracy too, but i takes less time
why does that happen? isn't sklearn doing the same thing we did ? 🤔

[priyanshumohanty]

Can someone explain why we do loss.item() here instead of simply loss, as done in previous tutorials?

[itsadira]

10:53 why do you need to call detach() at line #50 but not at line #34

[vl]

Hello! Why don't we iterate over n_samples in training loop?

[lucenHan]

thank you so musch, but i can't import datasets. could you help me?

[darkchoclate]

It aint clear what is the use of sklearn dataset, what does that function do, and all the parameters of the function do? can you please explain?

[HoangNguyen-bevy]

Why do we have to reshape the y (line 18) but not the X also?