Autoencoder In PyTorch - Theory & Implementation

In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.

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Timeline:
00:00 - Theory
02:58 - Data Loading
05:30 - Simple Autoencoder
15:02 - Training Loop
17:00 - Plot Images
19:00 - CNN Autoencoder
29:12 - Exercise For You

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[patloeber]

Let me know if you enjoyed the new animations in the beginning and want to see this more in the future :)

[maharshipathak]

For python 3.11+, pytorch 2.3+ change the dataiter.next() to next(dataiter)

[jh-pqtp]

big thanks to you. i cannot imagine how could i learn my dl course without your tutorial. Your work is the best in youtube so far!

[saadmunir]

Its reaallly nice but it would be a very nice addition to include variational autoencoders and Generative adversial networks as well :). Maybe they can be helpful to many struggling with class imbalance during classification

[astridbrenner]

This channel is so underrated.Please upload tutorials about Django

[starlite]

I love all your PyTorch videos, please do more :D

[ayankashyap]

at 22:17 when calculating the shape of the conv output, it should be 128*128*1 => 64*64 * 16 and the rest should also be different accordingly

[adityasaini]

Hey Patrick a really informative and concise video! Thoroughly enjoyed it :DD Just a small correction at 12:51, you used the word dimension while explaining the Normalize transform, whereas the two attributes are just the mean and standard deviation of the resultant normalized data.

[saeeddamadi]

Thank you so much for clear presentation of Autoencoder!

[shahinghasemi]

Thank you for your nice tutorials please do the same for a non-image data. I'm curious to see CNN auto-encoders with non-image data.

[falklumo]

It should be noted that the performance difference between Linear and CNN as shown here comes from the chosen compression factor. Linear chose 12 Byte per image, CNN chose 256 Byte per image, where an original image is 784 Byte. So, the CNN code does not compress enough, less than PNG actually! You need two more linear layers to compress 64 down to 16 and then 4.

[Mesenqe]

This channel is really good, I learned PyTorch from this channel. Guys I assure you subscribe to this channel.

[huoguo]

Great video! Could you provide the same walkthrough for a variational autoencoder? Or point point me to a good walkthrough on the theory and implementation of a variational autoencoder?

[ingenuity]

Thank you so much, you explained it really good.

[duzx]

This might be a stupid question, but why do the output images have no white border, because I thought we simply padded them to be 28x28, as otherwise they would be 27x27, no?

[saurrav]

Bro always waiting for your pyt🔥rch video ....🤙🏼🤙🏼🤙🏼

[garikhakobyan]

hello, nice videos you have. looking forward new videos on paper review and implementations.

[aisidamayomi]

Please can you do for a network intrusion detection

[devadharshan]

Great animations my suggestion is to add in more animations not only in theory but also in the working of the code . Just my suggestion but great video thanks for Ur teaching.

[martinmayo]

I don't understand a little bit the sintaxis. Why do you define the method 'forward' but never call it explicitly ? Maybe the line "recon = model(img)" is where you are using it, but I didn't know that it could be done like this. I would had written "recon = model.forward(img)", is it the same ?