Einsum Is All You Need: NumPy, PyTorch and TensorFlow

[@matt.jordan]

This is literally insane how well you explained this I instantly subbed you deserve so much more attention

[@AladdinPersson]

Wow thanks :)

[@udbhavprasad3521]

Honestly, there is no channel that even compares to this level of quality

[@misabic1499]

Hi. Your model building from scratch tutorials are really helpful. Eagerly waiting for more tutorials to come. I really appreciate it!

[@AladdinPersson]

I appreciate the kind words! Any video in particular that you thought were good and do you have any specific suggestions for the future?

[@qiguosun129]

This is literally the best and simplest explanation I ever had, thanks.

[@rajanalexander4949]

Excellent tutorial of a very useful but sometimes confusing feature in NumPy. I would only add that " . . . " is syntactic sugar for omitting a bunch of indices.

[@johngrabner]

Another perfect video. Most valuable because it provides a foundation for your other video. Can't wait for your next einsum video.

[@AladdinPersson]

Really appreciate your comment! :)

[@SantoshGupta-jn1wn]

One of the most important videos I've ever seen.

[@mayankkamboj4025]

Wow, I finally get einsum ! Thank you so much. And that lotr reference was good.

[@gauravmenghani4]

Lovely. I always found einsum non-intuitive. Learnt a lot! Thanks :)

[@stacksmasherninja7266]

It almost felt like you implemented these functions yourself in those libraries ! Great video

[@CrazyProgrammer16]

Hey, but why does "i,j->ij" also have a product??? Here in the input nothing is repeating. Are there other rules?

[@bourahmamasten4602]

nicely explained, thank you!

[@rekeshwardhanani920]

Insane brother, excellent just excellent

[@kenzhebektaniyev8180]

cool! tbh I didn't believe you could explain it but you did

[@iskrabesamrtna]

i had to translate it to tensorflow :) very useful video for practice. thank you!

[@thecros1076]

Learnt something new today❤️❤️, ...I always had a question how and were did you learn everything?

[@AladdinPersson]

I don't know all of this stuff. I research everything to try to make every video as good as I possible can so the process is usually that I learn something in depth and then decide to share it with you guys

[@thecros1076]

@@AladdinPersson ❤️❤️❤️loved all of your videos ... hardwork and talent is a deadly combination ....hope to see new project videos soon❤️

[@francesco_savi]

nice explanation, very clear! thanks!

[@valevan14]

This is great, I just wanna know however, if I can do FFT of Green function using einsum. Note: been trying for a week to implement the code, never got the correct result.

[@minma02262]

Thank you for sharing this!

[@iskhwa]

Thanks, a perfect explanation.

[@javidhesenov7611]

thanks for awesome explanation

[@leofh1917]

Thanx! This one is very useful!

[@parasharchatterjee3223]

It's the Einstein summation convention that's used in physics very commonly, and just removes the clunky summation sign in pages long calculations!

[@fergalhennessy775]

wow, the matrix diagonal operations feel almost like an abuse of notation to me. What would happen if the matrix wasn't square and had more rows than columns?

[@michaelmoran9020]

Are the "free indicies" part of standard einstein notation or something made up to allow you to exclude array dimensions from the einsum entirely?

[@Raghhuveer]

How does it compare in terms of performance and efficiency to standard numpy function calls?

[@danyalziakhan]

So, basically einsum is the DSL that is shared between these libraries, right?

[@haideralishuvo4781]

Awesome , Your channel is so underrated . Was struggling for a good channel to learn about pytorch ,Thanksfully got yours :D Can you cover pix2pix , cycleGAN , RCNN's ? Would be greatful if you do .

[@AladdinPersson]

Appreciate you 👊 Many people have requested that so it's coming but can't promise when :)

[@fergalhennessy775]

great video!

[@epolat19]

Does einsum mess the auto-differentiation of TensorFlow

[@iskrabesamrtna]

gives me error while matrix-vector multipication: torch.einsum("ij, kj->ik", x, v) einsum(): operands do not broadcast with remapped shapes [original->remapped]: [2, 5]->[2, 1, 5] [1, 3]->[1, 1, 3] same in tf Expected dimension 5 at axis 1 of the input shaped [1,3] but got dimension 3 [Op:Einsum]

[@deoabhijit5935]

are you considering doing an another video on advanced einsum?

[@johnj7883]

Thanks a lot. it saves my day

[@Han-ve8uh]

One thing that wasn't mentioned in the video that i realized halfway through is sometimes einsum is used on 1 operand while sometimes on 2. I tried "torch.einsum('ii->i', t,t)" and got "RuntimeError: einsum(): more operands were provided than specified in the equation". This tells me that the number of operands must correspond to the number of comma separated indexes on left hand side of ->.

[@iskrabesamrtna]

einsum to rule them all, indeed.

[@cassenav]

Great video thanks :)

[@alfahimmohammad]

will einsen work for model parallelism in keras models?

[@AladdinPersson]

I haven't tried that but I would imagine that it works

[@alfahimmohammad]

@@AladdinPersson I tried it. It wasn't good. I was better off with manually assigning each layer to each GPU in pytorch

[@SAINIVEDH]

can someone explain how matrix diagonal is "ii->i" ?

[@ericmink]

I think it's because if you wrote it as a nested loop, then you would loop over all rows with a variable `i`, and for the columns you would reuse the same variable (every entry at coordinates (i,i) is on the diagonal). Now for the result, if you left the `i` out it would sum the diagonal elements up. If you have it in there, it will create a list instead.

[@Choiuksu]

What a nice video !

[@AladdinPersson]

Thank you so much :)

[@jeanchristophe15]

I am not sure the "Batch matrix multiplication" example is correct, because i is used twice.

[@jamgplus334]

nicely done

[@rockapedra1130]

Very cool!

[@MorisonMs]

3:37 (Outer product) there is no need to sum, simply M[i,j] = A[i,k]*B[k,j]

[@lewis2865]

It's matrix multiplication

[@ALVONIUM]

Helt otroligt

[@gtg238s]

Great explanation! click

[@AladdinPersson]

Thank you so much! :)

[@ripsirwin1]

This is so difficult to understand I don't know if I'll ever get it.

[@AladdinPersson]

Sorry, maybe I didn't explain it good enough:/

[@ripsirwin1]

@@AladdinPersson no you're great. I just have to work at it

[@AlbertMunda]

awesome

[@AndyLee-xq8wq]

cool

[@hieunguyentrung8987]

np.einsum('ik,kj->ij', x,y) is actually much much slower than np.dot(x,y) when the matrix size gets larger Also tf.einsum is slightly slower than tf.matmul but torch.einsum is slightly faster than torch.matmul... Only from a perspective of the configuration of my laptop though