PyTorch Tutorial 09 - Dataset and DataLoader - Batch Training

New Tutorial series about Deep Learning with PyTorch!

In this part we see how we can use the built-in Dataset and DataLoader classes and improve our pipeline with batch training. See how we can write our own Dataset class and use available built-in datasets.

- Dataset and DataLoader
- Automatic batch calculation
- Batch optimization in training loop

Part 09: Dataset and DataLoader

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Official website:

Part 01:

Logistic Regression from scratch:

Code for this tutorial series:

You can find me here:

#Python #DeepLearning #Pytorch

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[rafiibnsultan]

This whole series is a gold mine, any one diving into PyTorch for the first time is highly recommended to follow this playlist.

[nlighten]

If anyone has issues with the data iterator object not having an attribute next. Instead of data = dataiter.next(), try doing data = next(dataiter). Worked for me

[n.w.]

Absolute perfect Tutorials Series as one can clearly learn each step on an easy to understand yet funny and "real world" example other than lots of other Tutorials that always end up with MNIST or something. Really appreciate all the work you clearly put in. Thank you and go on! 👌

[svennesvensson]

Ended up here after trying to get tensorflow 2.0 to work properly for 24h, and finally gave up and decided to learn PyTorch instead (installed in 2 min with no problems). Haven´t finished all the tutorials yet, but this is really top quality tutorials. Most tutorials on Youtube are poorly structured, but this could truly be it´s own Udemy course that you could charge for as someone mentioned (especially with the code repository included). Love the implementations from scratch where you gradually introduce PyTorch classes/methods by replacing code we built from scratch - this is truly a very pedagogic way to learn. Hats off to you sir!

[leromerom]

Very well explained, the logical sequence that you follow is fantastic, without a doubt one the most useful tutorials that I have seen, thank you for your contribution!

[gerben]

thanks man! the dataset and dataloader classes always confused me, but you explained it really clearly and now i successfully wrote my own dataset class.

[HasanKarakus]

You don't explain it normally. The most explanatory videos I've ever watched. Thanks for your efforts

[GeorgeAXxx]

This was an incredible video, thank you so so much! Gonna watch this entire series now.

[shadowchantghallanda]

Hi, Python Engineer, thanks for your intuitive tutorial. I just followed your tutorial 08 and succesfully implemented the logistic regression on the wine data set and got an accurancy of 91.67%.

[mohammadrashidi]

Very nice tutorials... Thanks a lot. I hope we see more posts from you in the fields of PyTorch and deep learning.

[ARPAN]

was confused about Dataset and Dataloader, and came across this. Have not finished it yet, but, I know, this is what I needed. Thanks for sharing your knowledge in such a nice way. Please keep them coming. Cannot thank you enough.
Subscribed right away.

[fahadrahmanamik]

Just wanna give you a hug for the awesome tutorial. Love you man ❤️

[wadewang]

very concise and informative ! I like such style's tutorial !

[timrorup]

For all of you who have multiprocessing issues: Change num_workers in dataloader = 0 instead of 2.

[utkumetin]

Thanks for great lessons, you are doing amazing job in here, keep going.

[henrygory]

The parallel workers did not work for me. I am using Win11. I had to set "num_workers=0" when calling DataLoader to get it to run.

[mlguru]

please keep uploading tutorials on pytorch, amazing tutorials!!!

[connorshorten]

Such a helpful reference, thanks again!

[killian]

This was very helpful. Great work, have a sub

[SuiGio]

Hi, great tutorial. I was wondering, what if we want to have separate features returned? Would we return x1, x2, y from __getitem__ ? Plus, should we return 2 lengths from __len__ ? Thanks