Large Language Models from scratch

[@somethingness]

This is so good. I can't believe it has so few views.

[@adarshraj1467]

Same, brillant explaination on NN

[@facqns]

Was just about to write the same.

[@webgpu]

if you really think so, post the link to this video on your social media.

[@itsm3th3b33]

So few views... If a Kardashian posts a brain fart it gets more views from the unwashed masses. That is the sad reality.

[@phil5053]

Very few study about it

[@triton62674]

This is seriously *really* good, I've not seen someone introduce high level concepts by-example so clearly (and nonchalantly!)

[@brendawilliams8062]

What have they done? amazing stuff

[@PabloMoscato]

Agree. I have it on one of my playlists now.

[@solotron7390]

Finally! Someone who knows how explain complexity with simplicity.

[@ivocamilleri8913]

This is an excellent articulation. We need part 3, 4, and 5

[@TheAkiller101]

if there is an Oscar for best tutorial on the internet, this video deserves it !

[@GregMatoga]

That might be the best, most concise and impactful neural network introduction I have seen to date

[@JohnLaudun]

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.

[@junglemandude]

Thanks, what a video, in 8 minutes I have learnet so much, and very well explained with graphics indeed.

[@SethWieder]

These visuals were SO HELPFUL in introducing and understanding some foundational ML concepts.

[@stratfanstl]

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.

[@DanielTateNZ]

This is t he best explanation of LLMs I've seen

[@ericsun1990]

I really liked your explanation of how "training a network" is performed. Made it a lot easier to understand

[@saqhorov]

this is excellently done, I'm very grateful for you putting this together.

[@ApocalypsoTron]

Thanks for showing what a neural network function looks like

[@AVV-A]

This is insanely good. I've understood things in 8 minutes that I could not understand after entire classes

[@funnycompilations8314]

you sir, deserve my subscription. This was so good.

[@jankshtt]

nice concise video explaining what is a large language model

[@marcusdrost8452]

Clearly explained! I will use it.

[@user-wr4yl7tx3w]

Wow. This is so well presented. And a different take that gets to the real intuition.

[@lopezb]

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...!!!!

[@noahnazareth8248]

Great video. "energy function" instead of error function, but a great explanation of gradient descent and backprop in a super short time. Excellent job!

[@lfmtube]

Brilliant! A truly example of intelligence and simplicity to explain! Thanks a lot.

[@erethir]

Agree with the other comments, so clear and easy to understand. I wish all teaching material was this good...

[@coderversion1]

Best and simplest explanation I have ever come across. Thank you sir

[@lipefml7200]

The best content ever I saw about the subject. Super dense and easy.

[@JessieJussMessy]

Being able to visualize this so simply is legendary. You're doing amazing work. Subbed

[@KalebPeters99]

Holy shit. This is one of the best KZbin videos I've seen all year so far. Bravo 👏👏👏

[@berbudy]

This video is a must watch

[@saurabhmaurya6380]

Straight away subscribed .... i would really love these videos in my feed daily.❤

[@liamcawley6440]

possibly the best explanation of LLM i've ever seen. accurate, pointed and concise

[@mmarsbarr]

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 !?

[@stan.corston]

Great way to explain a complex idea ⚡️

[@Luxcium]

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 🎉🎉🎉🎉

[@zeeg404]

What an amazing lecture on LLM! Loved the example Markov chain model with the bob Dylan lyrics, that was actually a fun homework exercise in one of my grad school courses. This really helped me understand neural networks, which are so much more complex.

[@laStar972chuck]

Stunning video of absolutely high and underrated quality !!!! Thanks so much, for this !

[@looppp]

This is an insanely good explanation. Subscribed.

[@pierrickguillard-prevert4213]

Thanks Steve, this explanation is just... Brillant! 😊

[@_sudipidus_]

This is so good I’m inspired to go back and learn Fourier and Taylor series

[@marktahu2932]

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.

[@SaidakbarP]

This is the best explanation of Large Language Models. I hope your channel gets more subscribers!

[@davidvarela6020]

This might be the highest signal to noise video I've ever watched

[@narasimhasriharshakanduri3325]

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

[@peregudovoleg]

Eventhough I knew all this stuff, it is still nice to watch and listen to a good explanation of these fundamental ML concepts.

[@BenyThePooh2]

This is uncut gold.

[@davhung8004]

Seems really really cool

[@sumitsp01]

Wow .. what an explanation sir ❤ Thank you 🙏

[@smithwill9952]

Clean and clear explaination

[@jancsi-vera]

Wow, just saw this 😂, its excellent, thank you

[@eriksteen84]

fantastic video, thank you!!!

[@go_better]

Thank you so much! Very well and simply explained!

[@samwynn3183]

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.

[@gregpavlik6474]

Super clear - need to circulate this around my teams adjacent to the scientists at work

[@hrishabhchoudhary2700]

The content is gem. Thank you for this.

[@Valeriy7D0]

such a great content! thank you!

[@dylan_curious]

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!

[@A338800]

Incredibly well explained! Thanks a lot!

[@vaguebrownfox]

Omgg are you serious? You have some top-notch pedagogical skills.

[@ravinatarajan4894]

Very nice illustration and fantastic explanation. Thanks

[@zhenli8674]

This is awesome. Very good Illustrations.

[@MrNilOrange]

This is brilliant.

[@jacobwilliams4182]

Great explanation of an advanced topic

[@kahoku451]

Incredible. Thank you

[@dragonwisp9810]

This video deserves more views.

[@AncientSlugThrower]

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!

[@imzhaodong]

Purely awsome

[@WoonCherkLam]

Probably one of the best explanations I've come across. :)

[@ВладимирСтанојевић]

Great video 👍! This reminds me of Markov Chain (MC). I read in some probability book long time ago that MC had been used to calculate probability of the next letter in a (randomly chosen) word.

[@lopezb]

It is exactly a Markov matrix, also known as a probability matrix, that is all the rows are probability vectors (nonnegative real numbers that sum to 1), like that used to define a Markov chain. If it depends only on the previous word, it's a 1-step chain (the usual kind); if on 3 words it's a k-step Markov chain, which can be re-coded as a 1-step chain by replacing the alphabet of symbols (words) by triples of symbols (triples of words). In fact, Markov himself used this model back in the 1930's to try to generate text. I found that out from this great talk by a UC Berkeley CS prof, author of the basic textbook in the field, and also of the "pause" letter: kzbin.info/www/bejne/imHOcn-vhLSNq5I&ab_channel=GoodTimesBadTimes Markov himself invented this to predict first words, and then word pairs in Eugene Onegin! (a 2-step version). Chat GPT is a 32,000 step version, but they have to train it stochastically or it would be way too much computation to use actual frequencies...

[@marlinhowley9858]

Excellent. Some of the best work I've seen. Thanks.

[@YusanTRusli]

very well explained!

[@darrin.jahnel]

Wow, what a fantastic explanation!

[@ashleygesty7671]

You are a genius, thank you for this amazing video!

[@shreeramshankarpattanayak7409]

Brilliantly explained !

[@mirabirhossain1842]

Unbelievably good video. Great work.

[@gigabytechanz9646]

Very clear and concise explanation! Excellent work!

[@LittleShiro0]

Very well explained. Thank you for the video!

[@BenKordick]

Awesome video! I really appreciated your explanation and representation of neural networks and how the number of nodes and weights affect the accuracy.

[@RK-fr4qf]

Fantastic. Please teach more You are a legend.

[@PinballWarlock]

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.

[@DimitriMissentos]

Amazingly insightful. Fantastically well explained. Thanks !

[@DavideVarvello]

wonderful, thank you so much for sharing

[@perseusgeorgiadis7821]

This was actually amazing

[@lucamatteobarbieri2493]

Simple and clear, kudos!

[@gregortidholm]

Really great description 👌

[@wokeclub1844]

such clean and lucid explanation. amazing

[@govinddwivedi582]

You are so good at explaining it! Please keep doing it.

[@jwilliams8210]

Outstanding!

[@CUDA602]

Amazing Video!

[@ammarlahori]

Absolutely brilliant..great examples

[@ryusei323]

This is fantastic. Thank you for sharing.

[@DragonZarman]

This video was ahead of its time

[@willg2834]

You’re a saint. This is incredible

[@bigdaddy-k2u]

this is gold, thanksss sir

[@sudhindrakopalle7071]

Fascinating and such wonderful explanation. Thank you very much!

[@KayYesYouTuber]

Great explanation. Thank you very much

[@StewartMcGinnis]

Majorly underviewed

[@danjsy]

Great ! Onto part 2 😃

[@mbrochh82]

Wow. This is incredible!!

[@reallylordofnothing]

to everyone who was enjoying it assuming that no background was required, wait till 03:47

[@jdemuro1]

Finally I’m not the only one. Thought I was taking crazy pills reading these comments.

[@brandonwashington4422]

This is literally gold

[@quantphobia2944]

Simply amazing, so intuitive..omg subscribed