how is it possible that i’ve watched a ton of videos trying to understand LLMs from the likes of universities and big tech companies yet this simple video in comic sans explains everything in the most direct and concise manner possible !?
I've been watching a lot of videos on LLMs and the underlying mathematics. This explanation is PHENOMENAL. Not dumbed down, not too long, and uses concepts of existing maths and graphing that cement the concept perfectly.
I have been working on ways to explain LLMs to people in the humanities for the past year. You've done it in 5 brilliant minutes. From now on, I'm just going to hand out this URL.
Great video. "energy function" instead of error function, but a great explanation of gradient descent and backprop in a super short time. Excellent job!
I loved this. Clarity = real understanding= respect for the curiosity and intelligence of the audience. Requests: Would like more depth about "back propagation", and on to why so many "layers" and so on...!!!!
i generally don't subscribe to any channels but this one deserves one. This takes a lot of understanding and love for the subject to do these kind of videos. thank you very much
Wow, this video was really informative and fascinating! It's incredible to think about how much goes into building and training a language model. I never realized that language modeling involved so much more than just counting frequencies of words and sentences. The explanation of how neural networks can be used as universal approximators was particularly interesting, and it's amazing to think about the potential applications of such models, like generating poetry or even writing computer code. I can't wait for part two of this video!
Wait a minute all day I try to understand what are neural networks and you have explained all parts so easily wow 😮 it obviously 🙄 imply that I have struggled to learn all of these terms so far but I finally have found a good explanation of back-propagation, gradient-descent, error functions and such 🎉🎉🎉🎉
Awesome video! I really appreciated your explanation and representation of neural networks and how the number of nodes and weights affect the accuracy.
I had not considered exactly how words related to eachother in automated texts and this video explained that concept in a really clear and concise way.
Great video. The example of the network with too few curve functions to recreate the graph really helped me understand how more or fewer nodes affects the accuracy of the result.
Great video! A lot covered super concisely. There is one minor issue I noticed though at 5:40 which you're probably aware of. Usually a bias term b is added inside the activation functions to get S(wx+b). Without this bias you severely limit the capacity/expressiveness of the network. For example, if we take S to be the ReLU function (0 if x
![The moment we stopped understanding AI [AlexNet]](/img/1.gif)
This is seriously *really* good, I've not seen someone introduce high level concepts by-example so clearly (and nonchalantly!)
What have they done? amazing stuff
Agree. I have it on one of my playlists now.
This is so good. I can't believe it has so few views.
Same, brillant explaination on NN
Was just about to write the same.
if you really think so, post the link to this video on your social media.
So few views... If a Kardashian posts a brain fart it gets more views from the unwashed masses. That is the sad reality.
Very few study about it
how is it possible that i’ve watched a ton of videos trying to understand LLMs from the likes of universities and big tech companies yet this simple video in comic sans explains everything in the most direct and concise manner possible !?
That might be the best, most concise and impactful neural network introduction I have seen to date
This is an excellent articulation. We need part 3, 4, and 5
These visuals were SO HELPFUL in introducing and understanding some foundational ML concepts.
if there is an Oscar for best tutorial on the internet, this video deserves it !
I've been watching a lot of videos on LLMs and the underlying mathematics. This explanation is PHENOMENAL. Not dumbed down, not too long, and uses concepts of existing maths and graphing that cement the concept perfectly.
Holy shit. This is one of the best RUclips videos I've seen all year so far. Bravo 👏👏👏
This is t he best explanation of LLMs I've seen
you sir, deserve my subscription. This was so good.
The best content ever I saw about the subject. Super dense and easy.
this is excellently done, I'm very grateful for you putting this together.
Wow. This is so well presented. And a different take that gets to the real intuition.
I have been working on ways to explain LLMs to people in the humanities for the past year. You've done it in 5 brilliant minutes. From now on, I'm just going to hand out this URL.
Straight away subscribed .... i would really love these videos in my feed daily.❤
Finally! Someone who knows how explain complexity with simplicity.
nice animations
I really liked your explanation of how "training a network" is performed. Made it a lot easier to understand
Thanks, what a video, in 8 minutes I have learnet so much, and very well explained with graphics indeed.
Great video. "energy function" instead of error function, but a great explanation of gradient descent and backprop in a super short time. Excellent job!
Being able to visualize this so simply is legendary. You're doing amazing work. Subbed
This is the best explanation of Large Language Models. I hope your channel gets more subscribers!
nice concise video explaining what is a large language model
This is an insanely good explanation. Subscribed.
Agree with the other comments, so clear and easy to understand. I wish all teaching material was this good...
Brilliantly explained !
Clean and clear explaination
Stunning video of absolutely high and underrated quality !!!!
Thanks so much, for this !
Brilliant! A truly example of intelligence and simplicity to explain! Thanks a lot.
I loved this. Clarity = real understanding= respect for the curiosity and intelligence of the audience.
Requests: Would like more depth about "back propagation", and on to why so many "layers" and so on...!!!!
Best and simplest explanation I have ever come across. Thank you sir
The content is gem. Thank you for this.
You have made it so easy to see and understand - it puts into place all the complicated explanations that exist out there on the net.
to everyone who was enjoying it assuming that no background was required, wait till 03:47
Finally I’m not the only one. Thought I was taking crazy pills reading these comments.
Thanks Steve, this explanation is just... Brillant! 😊
possibly the best explanation of LLM i've ever seen. accurate, pointed and concise
i generally don't subscribe to any channels but this one deserves one. This takes a lot of understanding and love for the subject to do these kind of videos. thank you very much
Thanks for showing what a neural network function looks like
Wow, this video was really informative and fascinating! It's incredible to think about how much goes into building and training a language model. I never realized that language modeling involved so much more than just counting frequencies of words and sentences. The explanation of how neural networks can be used as universal approximators was particularly interesting, and it's amazing to think about the potential applications of such models, like generating poetry or even writing computer code. I can't wait for part two of this video!
Wait a minute all day I try to understand what are neural networks and you have explained all parts so easily wow 😮 it obviously 🙄 imply that I have struggled to learn all of these terms so far but I finally have found a good explanation of back-propagation, gradient-descent, error functions and such 🎉🎉🎉🎉
Great way to explain a complex idea ⚡️
Simple and clear, kudos!
This is awesome. Very good Illustrations.
Excellent. Some of the best work I've seen. Thanks.
This is insanely good. I've understood things in 8 minutes that I could not understand after entire classes
Incredibly well explained! Thanks a lot!
This is so good
I’m inspired to go back and learn Fourier and Taylor series
Very clear and concise explanation! Excellent work!
Clearly explained! I will use it.
Great explanation of an advanced topic
This video is a must watch
Unbelievably good video. Great work.
Eventhough I knew all this stuff, it is still nice to watch and listen to a good explanation of these fundamental ML concepts.
This is uncut gold.
Very nice illustration and fantastic explanation. Thanks
Awesome video! I really appreciated your explanation and representation of neural networks and how the number of nodes and weights affect the accuracy.
Fantastic. Please teach more
You are a legend.
This was awesome. I don't think I could adequately explain how this all works yet, but it fills in so many gaps. Thank you for this video!
such clean and lucid explanation. amazing
Seems really really cool
Great explanation. Thank you very much
Amazing Video!
Very well explained. Thank you for the video!
such a great content! thank you!
Really great description 👌
Amazingly insightful. Fantastically well explained. Thanks !
You are so good at explaining it! Please keep doing it.
fantastic video, thank you!!!
Absolutely brilliant..great examples
This was actually amazing
This is brilliant.
Outstanding!
Wow .. what an explanation sir ❤
Thank you 🙏
You are a genius, thank you for this amazing video!
Wow, what a fantastic explanation!
Fascinating and such wonderful explanation. Thank you very much!
Thank you so much! Very well and simply explained!
Probably one of the best explanations I've come across. :)
This video deserves more views.
I had not considered exactly how words related to eachother in automated texts and this video explained that concept in a really clear and concise way.
Really well explained!!
Great video. The example of the network with too few curve functions to recreate the graph really helped me understand how more or fewer nodes affects the accuracy of the result.
Wow. This is incredible!!
Omgg are you serious? You have some top-notch pedagogical skills.
Incredible. Thank you
this is gold, thanksss sir
You’re a saint. This is incredible
This is fantastic. Thank you for sharing.
Simply amazing, so intuitive..omg subscribed
This video was ahead of its time
This is literally gold
very well explained!
Purely awsome
What a fantastic tutorial! Thank you! Liked and subscribed!
Really great explanation of LLM! Just earned a subscriber and I'm looking forward to more of your videos :)
Simply superb explanation
Great video! A lot covered super concisely. There is one minor issue I noticed though at 5:40 which you're probably aware of. Usually a bias term b is added inside the activation functions to get S(wx+b). Without this bias you severely limit the capacity/expressiveness of the network. For example, if we take S to be the ReLU function (0 if x
YES -- you are very observant 🙂 The video glosses over the bias parameter.
wonderful, thank you so much for sharing
Underrated channel!!!!