that was an interesting experience... i've seen some videos on this topic, most left me with more question marks then i had before. but this was somewhat like opening my brain and pouring a full book in there. like in matrix. i can't really tell what i've just learned, just that it was alot, and that it will be helpful later on. you surely are some dark magic ninja teacher :D
It’s insane just how informative this is. When I watch a Gonkee video, I expect a funny video, but this has taught me more than any wikipedia article ever could.
To be fair WP articles are mostly pretty shit on anything unless you already bring basic knowledge in the methodology a certain science field uses, it's pretty hard to make anything of a detail out of it.
Excellent, excellent video. So refreshing to have someone who pulls back a bit from the camera and doesn't talk in an obnoxious, gamer way, and instead explains very clearly and precisely, using helpful accompanying materials. Definitely one of the best deeper SD explanations.😃
Buddy, youtube needs more creators like you, I watched a lot of videos on stable diffusion but non of them made this much sense, please upload more deep learning content like this, the way you summarized computer vision is just amazing.
Before watching this video, I had no idea how any of this stuff worked. You were able to explain these concepts to me in a way that many other videos failed, and in only 30 minutes as well. Wow.
please make more videos like this regarding AI. I've learned so much from this in 30 min than the past 3 hours I've been studying. Informative, entertaining and hilarious. You sir a gem 🙌
21:01 yo I had to stop the video just after you said 'if you don't get anything from here on now don't stress out...', to tell you that I think you are amazing for daring to making videos like this, where you maybe don't have the biggest audience (yet), but provide people who actually care with so much value. I feel like you really stand out with this combination of explaining it in easy terms (in relation to the complexity of the topic) but sufficiently enough to give others an actual grasp of the topic and enabling people to make more advanced research afterwards. I think that in some weird sense the saying: "Give a Man a Fish, and You Feed Him for a Day. Teach a Man To Fish, and You Feed Him for a Lifetime". describes pretty well how I think about your content. Thank you very much for your efforts Gonkee and keep it going!
Excellent presentation. I especially like your statement, "If you have made it this far in the video? That's already pretty impressive." What is truly impressive is the level of detail that you deliver in a short amount of time. Thank you, ~3K
the "2 hours to connect to a printer" got me! Yesterday I tried it for the 4th time with my super smart hp knobhead. The little smart effer finally made it ^^
An important aspect of this that a lot of people misunderstand is that the things which are learned are kernels and visually semantic text embeddings. Which are very different from just memorizing/copying the training data, despite so many people insisting these models are, in fact, just copying/stealing training data. This is much more akin to how humans learn to make art: studying existing works, extracting those observations into features and techniques (kernels), learning how words describe visuals by... learning to talk, basically (visual semantic embeddings), and then combining it all to make new images. Although the diffusion model is more like "learning to remove stuff that doesn't match the requested visuals", there's a famous quote attributed to Michelangelo about how his sculpting is just "removing the marble that is not David" from the block -- very similar.
yea, by that logic every artist is basically stealing art, because he learns on and from already existing art and have that art influence his own work. those thinking ai is stealing art need to reconsider the way art is actually made. people don't understand the concept at hand, and they are afraid of things they don't understand, and that is the actual problem here.
@@algee2005They're afraid of the consequences of capitalism, but they either don't realize that or they don't want to blame capitalism, so they project their fears onto the AI that's making them face it and make up rationalizations for why they think we should fear the AI. Capitalism is what enforces the idea of "if you can't sell it, then it's not worth doing and you deserve to die"; AI isn't what says that.
@@algee2005Yeess! That was exactly what I was thinking when I saw artists say I demand to get payed for these models trained on my art. But duude what about all the people that like your art and are learning how to replicate it, should they pay you as well? NO, lmao. You put it up for the public. One consequence of that is that other people (meat or machine) can learn your style. And I'm pretty sure he himself learned from others.
Although i get the idea that " An important aspect of this that a lot of people misunderstand is that the things which are learned are kernels and visually semantic text embeddings" - what I do not understand is in the training process how text is injected (it is almost at the end 29:37)"
@@ArunAthavale-z5jLike it says in the video: where an attention head would normally calculate its key and query vectors from the same input -- the same text, for instance, or the same image -- these models instead calculate the query vector from the image tokens, and the key vector from the text embeddings. As such, at each attention block, both the image and text semantics are used when determining the meaning of the overall prompt.
Stable Diffusion finally make sense. I watched many other videos and they always talk about "denoising" an image which makes absolutely no sense when it comes to image generation (by itself). This is exactly when some creators explain generative AI as just "next-token-predictor" as if this is sufficient to explain how it really works. Thank you for the wonderful video and the geniuses behind these technologies.
I found your channel close to 3 years ago off a reddit post and commented on your very first video. I did not know this was the same person going into this video, and I was absolutely MIND BLOWN. Insane progress and I am so proud of your videos. This video was incredible and actually blew my mind. Great job.
Thank you! I've read half a dozen blog posts where it was clear the author had no idea how it all worked, then finally I saw your video and got a great overview of the whole thing!
I've spent the last few weeks consuming 10s of hours of content on deep learning from neural nets through transformers and found this when trying to get into stable diffusion. Honestly, this is some of the best informative content I've ever consumed - totally outdoes everything else on coursera/youtube/wherever for visual explanation. Plus you got bants as a bonus, what a guy, please keep it up!
Literally the BEST video on this topic on the sight, everyone else fills too much jargon but you keep it straight forward and break down concepts Very well
Gonkee, you are a gifted trainer/explainer! This video makes so much sense (even though I am a total noob) because your visualizations walk through the concepts so beautifully. I am so glad I found this!! I now follow you and look forward to reviewing your other vids. Thank you for helping me (not a math person, but an artist and visual learner) become more familiar with how AI image generation works.
Thanks for the video! The receptive field of each convolution is also effectively enlarged by doing several convolutions in sequence, not just by pooling operations.
bro god bless you for how much effort you put into this, you have learned what people study in uni for years, i look up to you, thank you so much for the educational content
This is an amazing video, I've been struggling with the concept, well, and laziness but this video has inspired me to continue ny project which contains some aspect of stable diffusion, thank you
Thank you for the encouragement, I’m at the 21 minute mark and I’ve struggled despite your clear and straightforward explanations thus far. Great video.
I've seen your previous videos and, as a grad student majoring in AI, I'm very impressed by how you delivered! Learned a lot here, so definite thumbs up and share!!
The "London - England + Japan = Tokyo" example really blew my mind, because it makes totally sense, yet I never saw this logic approach this way before. Think of "London" and every aspect of it: This is the Vector (Mind-Map). Now remove the Vector of "England" from that London-Vector, and you will be left with the most prominent aspect of it: "A Capital City". Add the "Japan"-Vector to that and you basically end up with "Capital City of Japan"
Even though I probably won't make use of the knowledge in the close future, I watch your videos in general becase the topics and your explanation are top notch. I really wish we had uni teachers/tutors like you. Straight to the point, no bullshit, keeping it interesting, maintaining the attention of the viewer, well done! 10/10
I'm working on a very new approach I call Focus-Select-Image-Generation, which is an image-evolution process from often clicking real-time generated thumbnails almost like the image-enhancing process. Main difference is... we can have full control to get what results we want, although not as a text-to-image generator. My version will still require A.I. for it to capture important details such as correct light refractions, correct proportions, correct shadowing... etc.
Sure you’re not some future tech AGI sent to help us get to your timeline? No other explanation for such an extensive lossless compression into 30mins. Bravo!
Appreciate your work. Although I could not grasp many things explained, I realized this is a darn lot of processing & amazing to just being able to type and get a video clip or an image!
@Gonkee This is a great video. However, I've watched a lot of videos on Stable Diffusion recently, and I've found that there are a few important questions that none of them have addressed. Perhaps you could make an addendum and address these questions because I'm sure many people want to know. 1. Is the "seed" for Stable Diffusion the same as an image containing maximum noise? 2. Does Stable Diffusion use a randomly generated image with maximum noise as the starting point for the new image it will generate? 3. If so, are the pixels in this maximum noisy image truly random, or do the pixels already have a pre-chosen mathematical relationship to adjacent pixels or to images from the training library? 4. The training of the neural network appears to work in 2D with the pixels of the training images, but the images are usually 2D representations of 3D objects. Does the network develop an understanding of the 3D characteristics of the objects in the images, or does it strictly build up probabilities of pixel colors (or something else) based on pixel color patterns from the training images? 5. When building a new image, does Stable Diffusion just find ways to insert the pixels of components or subcomponents from objects in images from the training library into the new image, or does it build the images strictly based on the likely pixel colors (pixel by pixel) deduced from the neural network, or some other process entirely? 6. Do you think the way Stable Diffusion builds new images is similar to how the human brain can see objects or animals in a Rorschach inkblot test or in the shape of clouds in the sky, or would you say the process is not similar?
Terrific Explanation, Thanks ! Now I understand how clicking a button will give you a verbal description of an image (just the reverse of the text prompt).
Bravo! Well done. The right dose of mathematics and the mention of U-Net make it really easy to understand the basic concepts of how stable diffusion works.
Basically like traditional painting using the ala prima method, but instead of handling brush strokes everywhere in the canvas evenly, it's a handles every damn pixel in the screen.
It actually solves the equation: ``` 241 - (-241) + 1 ChatGPT To solve the expression, let's follow the order of operations: Start with the subtraction inside the parentheses: (-241) = -241. Now we have 241 - (-241) + 1, which becomes 241 + 241 + 1. Add the numbers: 241 + 241 = 482. Finally, add 482 + 1 = 483. Therefore, 241 - (-241) + 1 equals 483. ``` If you want it to fail really good, even for 4, you should get very randomish numbers between 10.000 and 100.000 and do X + Y * Z
This is a great video, but why is it shifting around most of the time? All of the visual presentation is jumping around in the frame by a few pixels every 1/2 of a second or so, it's extremely annoying and distracting trying to absorb the info.
Thankz 4 uploading, I'm having fun with these Ai generators, I saw a Diffusion footage a month ago & said 'how' funny enough I shot some footage 2day that I hope I will b able 2 put Diffusion 2 work, thankz 4 the explanation, it makes sense 2 me cause I'm a computer geek from college dayz 1984 when I left & I've been working with them until now, the future is Ai like your self I'm not concerned about Ai taking over, cause u can just pull the plug. Guidance & Protection Mannerz & Respect!!
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
Get NordVPN 2Y plan + 1 months free here ➼ nordvpn.com/gonkee It’s risk-free with Nord’s 30-day money-back guarantee!
Could you do more videos about 2D soft bodies, 2D physics simulation? Your videos super helpful.
that was an interesting experience... i've seen some videos on this topic, most left me with more question marks then i had before. but this was somewhat like opening my brain and pouring a full book in there. like in matrix. i can't really tell what i've just learned, just that it was alot, and that it will be helpful later on. you surely are some dark magic ninja teacher :D
It’s insane just how informative this is. When I watch a Gonkee video, I expect a funny video, but this has taught me more than any wikipedia article ever could.
Agreed 👍
To be fair WP articles are mostly pretty shit on anything unless you already bring basic knowledge in the methodology a certain science field uses, it's pretty hard to make anything of a detail out of it.
Excellent, excellent video. So refreshing to have someone who pulls back a bit from the camera and doesn't talk in an obnoxious, gamer way, and instead explains very clearly and precisely, using helpful accompanying materials. Definitely one of the best deeper SD explanations.😃
This is, by far, the best explanation I've come across in my search to understand how SD is doing what it does. Bravo sir, and thank you!
Buddy, youtube needs more creators like you, I watched a lot of videos on stable diffusion but non of them made this much sense, please upload more deep learning content like this, the way you summarized computer vision is just amazing.
Before watching this video, I had no idea how any of this stuff worked. You were able to explain these concepts to me in a way that many other videos failed, and in only 30 minutes as well. Wow.
please make more videos like this regarding AI. I've learned so much from this in 30 min than the past 3 hours I've been studying. Informative, entertaining and hilarious. You sir a gem 🙌
21:01 yo I had to stop the video just after you said 'if you don't get anything from here on now don't stress out...', to tell you that I think you are amazing for daring to making videos like this, where you maybe don't have the biggest audience (yet), but provide people who actually care with so much value. I feel like you really stand out with this combination of explaining it in easy terms (in relation to the complexity of the topic) but sufficiently enough to give others an actual grasp of the topic and enabling people to make more advanced research afterwards. I think that in some weird sense the saying: "Give a Man a Fish, and You Feed Him for a Day. Teach a Man To Fish, and You Feed Him for a Lifetime". describes pretty well how I think about your content. Thank you very much for your efforts Gonkee and keep it going!
Excellent presentation. I especially like your statement, "If you have made it this far in the video? That's already pretty impressive."
What is truly impressive is the level of detail that you deliver in a short amount of time.
Thank you,
~3K
the "2 hours to connect to a printer" got me! Yesterday I tried it for the 4th time with my super smart hp knobhead. The little smart effer finally made it ^^
An important aspect of this that a lot of people misunderstand is that the things which are learned are kernels and visually semantic text embeddings. Which are very different from just memorizing/copying the training data, despite so many people insisting these models are, in fact, just copying/stealing training data. This is much more akin to how humans learn to make art: studying existing works, extracting those observations into features and techniques (kernels), learning how words describe visuals by... learning to talk, basically (visual semantic embeddings), and then combining it all to make new images. Although the diffusion model is more like "learning to remove stuff that doesn't match the requested visuals", there's a famous quote attributed to Michelangelo about how his sculpting is just "removing the marble that is not David" from the block -- very similar.
yea, by that logic every artist is basically stealing art, because he learns on and from already existing art and have that art influence his own work. those thinking ai is stealing art need to reconsider the way art is actually made. people don't understand the concept at hand, and they are afraid of things they don't understand, and that is the actual problem here.
@@algee2005They're afraid of the consequences of capitalism, but they either don't realize that or they don't want to blame capitalism, so they project their fears onto the AI that's making them face it and make up rationalizations for why they think we should fear the AI.
Capitalism is what enforces the idea of "if you can't sell it, then it's not worth doing and you deserve to die"; AI isn't what says that.
@@algee2005Yeess! That was exactly what I was thinking when I saw artists say I demand to get payed for these models trained on my art. But duude what about all the people that like your art and are learning how to replicate it, should they pay you as well? NO, lmao. You put it up for the public. One consequence of that is that other people (meat or machine) can learn your style. And I'm pretty sure he himself learned from others.
Although i get the idea that "
An important aspect of this that a lot of people misunderstand is that the things which are learned are kernels and visually semantic text embeddings" - what I do not understand is in the training process how text is injected (it is almost at the end 29:37)"
@@ArunAthavale-z5jLike it says in the video: where an attention head would normally calculate its key and query vectors from the same input -- the same text, for instance, or the same image -- these models instead calculate the query vector from the image tokens, and the key vector from the text embeddings. As such, at each attention block, both the image and text semantics are used when determining the meaning of the overall prompt.
Stable Diffusion finally make sense. I watched many other videos and they always talk about "denoising" an image which makes absolutely no sense when it comes to image generation (by itself). This is exactly when some creators explain generative AI as just "next-token-predictor" as if this is sufficient to explain how it really works. Thank you for the wonderful video and the geniuses behind these technologies.
I found your channel close to 3 years ago off a reddit post and commented on your very first video. I did not know this was the same person going into this video, and I was absolutely MIND BLOWN. Insane progress and I am so proud of your videos. This video was incredible and actually blew my mind. Great job.
Same
27:51 break; literally 😂 ❤love your style. Congratulations
Thank you! I've read half a dozen blog posts where it was clear the author had no idea how it all worked, then finally I saw your video and got a great overview of the whole thing!
I've spent the last few weeks consuming 10s of hours of content on deep learning from neural nets through transformers and found this when trying to get into stable diffusion. Honestly, this is some of the best informative content I've ever consumed - totally outdoes everything else on coursera/youtube/wherever for visual explanation. Plus you got bants as a bonus, what a guy, please keep it up!
Literally the BEST video on this topic on the sight, everyone else fills too much jargon but you keep it straight forward and break down concepts Very well
Gonkee, you are a gifted trainer/explainer! This video makes so much sense (even though I am a total noob) because your visualizations walk through the concepts so beautifully. I am so glad I found this!!
I now follow you and look forward to reviewing your other vids. Thank you for helping me (not a math person, but an artist and visual learner) become more familiar with how AI image generation works.
Thanks for the video!
The receptive field of each convolution is also effectively enlarged by doing several convolutions in sequence, not just by pooling operations.
Thanks for making this! Learned a ton and didn't need to get a computer science degree first to understand it!
bro god bless you for how much effort you put into this, you have learned what people study in uni for years, i look up to you, thank you so much for the educational content
Absolutely wonderful effort. Much liked and appreciated, Gonkee.
This is an amazing video, I've been struggling with the concept, well, and laziness but this video has inspired me to continue ny project which contains some aspect of stable diffusion, thank you
Thank you for the encouragement, I’m at the 21 minute mark and I’ve struggled despite your clear and straightforward explanations thus far. Great video.
very impressed by Gonkee's intuitive grasp of semantic segmentation and U-net. One of the best lecture
I've seen your previous videos and, as a grad student majoring in AI, I'm very impressed by how you delivered! Learned a lot here, so definite thumbs up and share!!
Is this taught in your university program?
@@goodbrainwork no
@@seyedmatintavakoliafshari8272 what they teach instead of this?
The "London - England + Japan = Tokyo" example really blew my mind, because it makes totally sense, yet I never saw this logic approach this way before.
Think of "London" and every aspect of it: This is the Vector (Mind-Map). Now remove the Vector of "England" from that London-Vector,
and you will be left with the most prominent aspect of it: "A Capital City". Add the "Japan"-Vector to that and you basically end up with "Capital City of Japan"
Even though I probably won't make use of the knowledge in the close future, I watch your videos in general becase the topics and your explanation are top notch. I really wish we had uni teachers/tutors like you. Straight to the point, no bullshit, keeping it interesting, maintaining the attention of the viewer, well done! 10/10
You have an amazing ability to teach with visuals to make intricate concepts accessible.
I'm working on a very new approach I call Focus-Select-Image-Generation, which is an image-evolution process from often clicking real-time generated thumbnails almost like the image-enhancing process. Main difference is... we can have full control to get what results we want, although not as a text-to-image generator. My version will still require A.I. for it to capture important details such as correct light refractions, correct proportions, correct shadowing... etc.
the best video on stable diffusion out there , that deserve so much more what it gets now , i can see how much efforts he put frame by frame
Finally, you didn't make the video too informative or too funny. I like this kind of videos. Keep up the good work! (im waiting for 1M subs)
One of the best video tha I have ever seen about AI.
wake up babe new gonkee vid just dropped
Sure you’re not some future tech AGI sent to help us get to your timeline? No other explanation for such an extensive lossless compression into 30mins. Bravo!
Appreciate your work. Although I could not grasp many things explained, I realized this is a darn lot of processing & amazing to just being able to type and get a video clip or an image!
This was a fabulous explanation! Makes it way more trivial to wrap my head around!
The most exhaustive explanation of stable diffusion on youtube
This is the first video I have watched on this channel and I am absolutely wowed by the way you have explained it all. Thanks a ton.
@Gonkee
This is a great video. However, I've watched a lot of videos on Stable Diffusion recently, and I've found that there are a few important questions that none of them have addressed. Perhaps you could make an addendum and address these questions because I'm sure many people want to know.
1. Is the "seed" for Stable Diffusion the same as an image containing maximum noise?
2. Does Stable Diffusion use a randomly generated image with maximum noise as the starting point for the new image it will generate?
3. If so, are the pixels in this maximum noisy image truly random, or do the pixels already have a pre-chosen mathematical relationship to adjacent pixels or to images from the training library?
4. The training of the neural network appears to work in 2D with the pixels of the training images, but the images are usually 2D representations of 3D objects. Does the network develop an understanding of the 3D characteristics of the objects in the images, or does it strictly build up probabilities of pixel colors (or something else) based on pixel color patterns from the training images?
5. When building a new image, does Stable Diffusion just find ways to insert the pixels of components or subcomponents from objects in images from the training library into the new image, or does it build the images strictly based on the likely pixel colors (pixel by pixel) deduced from the neural network, or some other process entirely?
6. Do you think the way Stable Diffusion builds new images is similar to how the human brain can see objects or animals in a Rorschach inkblot test or in the shape of clouds in the sky, or would you say the process is not similar?
Thanks for asking these questions as many of them have perplexed me and I cant find any resources that give an explanation.
Damn the video is detailed but at the same time easy to understand for a beginner person like me. Thanks for the video!
Terrific Explanation, Thanks ! Now I understand how clicking a button will give you a verbal description of an image (just the reverse of the text prompt).
This really made a lot of sense. I understood self-attention before, and now I magically know cross-attention! Thanks
This is such an impressive video, I remember you from your first videos and it’s so impressive how far you’ve come
Love your hoodie
Congratz on the 110.000 my dude, absolutely crazy!
Bravo! Well done. The right dose of mathematics and the mention of U-Net make it really easy to understand the basic concepts of how stable diffusion works.
Great video very well explained. Would love to see a similar video on LLMs
Amazing job breaking this down, highly comprehensible!
It clarifies a lot about how these generative ais works. Thank you!!
Wow. I need a break. You can't teach old dog new tricks. But I am trying. Awesome effort. Thanks for sharing.
this blew my mind! But was totally digestible. Thanks for making this!
I should note that if you are having trouble connecting to a wireless printer (or a chromecast in my case) check that you're _not_ connected to a VPN
Lol
WTF how does this only have 31K views, i was sure it would be at least 500K minimum.
Basically like traditional painting using the ala prima method, but instead of handling brush strokes everywhere in the canvas evenly, it's a handles every damn pixel in the screen.
awesome video, helped me grasp a pretty complex concept
Yay Gonkee video
Yay Gonkee video
Yay Gonkee video
Yay Gonkee video.
Dude you are a G.
no, he is not.
Simply the best video to stable diffusion I found so far. Thanks a lot! I like your humor.
This man has an IQ of 200 and explains complex stuff like a champ !
I’m revisiting this video after having learned ComfyUI. Much easier to understand Stable Diffusion now.
It actually solves the equation:
```
241 - (-241) + 1
ChatGPT
To solve the expression, let's follow the order of operations:
Start with the subtraction inside the parentheses: (-241) = -241.
Now we have 241 - (-241) + 1, which becomes 241 + 241 + 1.
Add the numbers: 241 + 241 = 482.
Finally, add 482 + 1 = 483.
Therefore, 241 - (-241) + 1 equals 483.
```
If you want it to fail really good, even for 4, you should get very randomish numbers between 10.000 and 100.000 and do X + Y * Z
Shovel == sold. Subbed.
I understood like half of it, can’t wait to start with CSE 👍 great video regardless
Finally, i have some intuition of what query key value might mean 🙏
Love your channel bro
query key and value are just SVD, they correspond like query = U, key = Sigma and value = V 26:12
This is next level! I owe my understanding of these fundamentals to you. Keep up the amazing work bro!
Crazy good video nice job gonk
Thank you for this masterpiece of STEM education. Incredibly well made.
Great explanation for people of all levels. Keep it up!!
This is a great video, but why is it shifting around most of the time? All of the visual presentation is jumping around in the frame by a few pixels every 1/2 of a second or so, it's extremely annoying and distracting trying to absorb the info.
Your explanation of this topic is just amazing! Thank you!
This is one of the best videos on ai
This is really good！ Deep，conceptually accurate, and clear! Thank you for sharing!
Wow this video is amazing, really well made, and informative, thank you
This is really good. I actually got most of it until the cross attention part.
Best statement of AI-shock so far. 0:20
P.S. I'll listen hard. Yeah, I've added this to my Sleep playlist , but that doesn't mean anything.
great video. Could youplease provide relevant papers for each concept you explained?
A great self explanatory video full of information...
Amazing! Thank you so much for making this :D
Oh my god how haven’t I seen this channel earlier.
Finally, a nerdy Gonkee video
Can you please share how you did research for this video or what sources did you use most heavily?
bruh .. I can't describe in words how amazing this video is!
Insane explanation. One of the best.
Gonkee VIDEOO LETSS GOO!!!
This is pure quality this is insane.I want you to explain transformer architecture for next
Thankz 4 uploading, I'm having fun with these Ai generators, I saw a Diffusion footage a month ago & said 'how' funny enough I shot some footage 2day that I hope I will b able 2 put Diffusion 2 work, thankz 4 the explanation, it makes sense 2 me cause I'm a computer geek from college dayz 1984 when I left & I've been working with them until now, the future is Ai like your self I'm not concerned about Ai taking over, cause u can just pull the plug.
Guidance & Protection
Mannerz & Respect!!
Self-attention in stable diffusion is image token to image token attention, not text token to text token attention.
It's kinda amazing that i'm also learning this video using neural network of my brain.
Like, AI exist because of brain to begin with afterall.
excellent video, i'm ready to go in the most absolute techinical part of it, thanks
The only explanation which I was looking for, Ausome Brother❤
Very well explained. Congrats!
Such a wonderful explanation. Bravo! I learned a lot from you. I wish i could find your personal website or google scholar😅
Why isn't everybody just explaining like you do? They want us to think that we need a BACHELOR'S DEGREE?? 😭😭😭🙏
Very well explained , i feel lucky to have clicked your video
Awesome video
Wow your explanations are brilliant, thank you.
Awesome explanation, really clear and excellent visuals. Sure to be a source of information for many in the future, thanks!!