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This is such an amazing video. Thank you.

straight heat i can't even lie good stuff bro

fye

I feel silly for asking, but the different colored blocks (in the middle) correspond to convolutions over different channels of the original matrix right?

i finally understood why convolution makes more channels. thank you so so much

So the point he is trying to make is that an "image" is represented as a 3d object/array , that is height(pixels) , width (pixels) and RGB components, but when we talk about 2d images we usually means a "grayscale image" , which doesn't require the RGB part mentioning explicitly, although it is still a 3d image , . So for a colored image of 128x128 pixels , tenser shape would be (128,128,3) & for a grayscale image it would be (128,128,1)

Is feature and filter count the same as PyTorch channel size?

The convolution is the same, you're just using different dimensions and then using the same dimension label for the operation. You're comparing apples to oranges.

these are the best animations i've seen about neural nets, i hope we can get a such a clear video like the one in separable dephwise convolutions but for attention

An absolute pinnacle of online education materials in the field, when it comes to giving a real gut intution of what do operation look like 🙌 its a real talent you got there, thank you on behalf of the rest of the internet for using it well

This helped so much, you can't understand how thankful I am

game changer for anyone learning neural networks

Well explained.

Nice video, you should post more .

Thanks for that. It was really confusing before your animation came up!

So in case of a feature map input, 2d conv just replicate each 2d filter along the feature dimension and do multiplication wise? In the video, the filters are 2d really just replicate to fill in the the number of features? or does each 2d filter is in reality a 3d tensor to match the feature dimension?

I have been struggling to mentally visualize convolutions, specially going from one dimension to others. I was reading the book Understanding Deep Learning by Simon Prince and I realized what i thought i looked like was wrong ( The 2D to 2D animations from the beginning). I wish I would have stumbled upon yours before having to imagine what was explained in the books XD (Good book tho)

Such a beautiful video

Thank you. Great job

wanna bet e by e would be somehow mathematically optimal?

Coming here after "All convolution animations are wrong", brilliant work! thank you very much!

The other drawings and visuals can't keep up with this! Great content! I love the visualizations!

My second favourites 3Brown1Blue channel

Please do a video on backpropagation (since its another convolution)

thx

Why is your input tensor so many dimensions? Shouldn’t the depth be only 3 (1 for each color channel)?

These videos are outstanding! Finally, true visualisations that get it right. I'm sharing these with my ML Masters students. Thank you for your considerable effort putting these together.

I like it, really, love it! But... I don't see what's wrong with other illustrations and peculiarly I think yours just iterates what they already clearly illustrate. I was even expecting CNN representations in XYZ visuals. Am I missing some points here? Honest question, would appreciate any enlightenment! (btw, thank you for sharing the world with your own version of splendid animation!) PS: If you're up for the challenge, do Spiking NN, I'll buy you a beer in Bali!

thx!!

Its funny you mention that the number of kernels is the least exciting part, my thesis was an attempt on finding a systematic way to reduce the number kernels by correlating them and discarding kernels that “extract roughly the same features”. Great video!

jiff

The reason the filter at 3:00 being 2D gets glossed over is because most image signal processing is taught in grayscale

2:13 how does it stay at the same size? Padding the edges of the original image?

What actually is the 3rd dimension in this context for the source giant cube? Is that multiple colors? A batch of multiple images?

Lol grayscale is a real thing still! Medical and microscopy imaging

this is awesome and is inspiring me to learn blender!

0:16 If filters are stored in a 4-dimensional tensor and one of them represents the number of filters, then what does the depth represent?

I have some hobbyist signal processing experience of a few decades, and these new methods seem so amateurish compared to what we had in the past. FFT, FHT, DCT, MDCT, FIR filters, IIR filters, FIR design based on frequency response, edge adapted filters (so no need for smaller outputs), filter banks, biorthogonal filter banks, window functions, wavelets, wavelet transforms, laplacian pyramids, curvelets, counterlets, non-separable wavelets, multiresolution analysis, compressive sensing, sparse reconstruction, SIFT, SURF, BRISK, FREAK, yadda yadda. Yes we even had even length filters, and different filters for analysis than for synthesis.

Incridibly helpful, keep up the good work!

I'm confused because you make it look like an attention layer could be used as a drop-in replacement for a linear layer but GPT-4o says: "No, an attention layer cannot be used as a direct drop-in replacement for a linear layer due to the fundamental differences in their functionalities and operations."?

Sir, Thanks for doing god's work!!! I wonder why this channel has so few viewers; it deserves to be known by more people. Deep Learning is much simpler if learned from this guy. Honestly, I truly admire you for taking the time to research and visualize something so complex, making it easy for everyone to understand.

This is so unfairly underrated, I have never seen such a good video about CNNs.

Which is correct?????

Stride value was 2 pixel

You did not introduce convolution in any informative way, nor define any terms for your argument, and you didn't explain the purpose of 3D convolution or why 2D convolution is inaccurate in the first place. There is also no closing argument for what appears to be your proposition for the proper illustration of CNN. This whole video is completely open ended and thus ambiguous.

Loved the animation thank you!!

Excelent!!

cool even better add names to the objects like kernel etc would be helpful to new people

I adore your content, genuinely can’t wait for more videos of your visualizations. Feels like I’m building real intuition about what I’m doing watching you :)

keep it up great videos