Large Language Models from scratch

How do language models like chatGPT and Palm work? A short cartoon that explains transformers and the tech behind LLMs.
Part 2: • Large Language Models:...
0:05 - autocomplete
0:44 - search query completion
1:03 - language modeling with probabilities
1:59 - time series and graphs
2:34 - text generation
3:43 - conditional probabilities
3:52 - trigrams
4:49 - universal function approximation
5:19 - neural networks
6:33 - gradient descent
7:03 - back propagation
7:24 - network capacity

[@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.

[@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

[@solotron7390]

Finally! Someone who knows how explain complexity with simplicity.

[@ivocamilleri8913]

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

[@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.

[@SethWieder]

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

[@GregMatoga]

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

[@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.

[@junglemandude]

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

[@ericsun1990]

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

[@TheAkiller101]

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

[@DanielTateNZ]

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

[@KalebPeters99]

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

[@user-wr4yl7tx3w]

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

[@funnycompilations8314]

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

[@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!

[@saqhorov]

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

[@AVV-A]

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

[@SaidakbarP]

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

[@JessieJussMessy]

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

[@coderversion1]

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

[@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...!!!!

[@jankshtt]

nice concise video explaining what is a large language model

[@liamcawley6440]

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

[@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 🎉🎉🎉🎉

[@saurabhmaurya6380]

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

[@ApocalypsoTron]

Thanks for showing what a neural network function looks like

[@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.

[@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!

[@lipefml7200]

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

[@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 !?

[@lfmtube]

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

[@BenKordick]

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

[@noahlin3851]

This video was ahead of its time

[@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

[@jonathanpersson9656]

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

[@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.

[@pierrickguillard-prevert4213]

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

[@laStar972chuck]

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

[@looppp]

This is an insanely good explanation. Subscribed.

[@gigabytechanz9646]

Very clear and concise explanation! Excellent work!

[@marcusdrost8452]

Clearly explained! I will use it.

[@darinmcbride4003]

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.

[@stan.corston]

Great way to explain a complex idea ⚡️

[@_sudipidus_]

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

[@wokeclub1844]

such clean and lucid explanation. amazing

[@BeSlaying]

Really great explanation of LLM! Just earned a subscriber and I'm looking forward to more of your videos :)

[@govinddwivedi582]

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

[@shreeramshankarpattanayak7409]

Brilliantly explained !

[@smithwill9952]

Clean and clear explaination

[@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!

[@ravinatarajan4894]

Very nice illustration and fantastic explanation. Thanks

[@DimitriMissentos]

Amazingly insightful. Fantastically well explained. Thanks !

[@marlinhowley9858]

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

[@BenyThePooh2]

This is uncut gold.

[@sumitpawar000]

Wow .. what an explanation sir ❤ Thank you 🙏

[@mirabirhossain1842]

Unbelievably good video. Great work.

[@labCmais135]

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

[@WoonCherkLam]

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

[@zhenli8674]

This is awesome. Very good Illustrations.

[@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.

[@davidvarela6020]

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

[@s0meus3r]

🎉 I know I need to write something to promote this video. It deserves that

[@berbudy]

This video is a must watch

[@lucamatteobarbieri2493]

Simple and clear, kudos!

[@gregpavlik6474]

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

[@A338800]

Incredibly well explained! Thanks a lot!

[@sudhindrakopalle7071]

Fascinating and such wonderful explanation. Thank you very much!

[@hrishabhchoudhary2700]

The content is gem. Thank you for this.

[@peregudovoleg]

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

[@vaguebrownfox]

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

[@gregortidholm]

Really great description 👌

[@go_better]

Thank you so much! Very well and simply explained!

[@LittleShiro0]

Very well explained. Thank you for the video!

[@stanleytrevino6735]

This is a very good video! Excellent explanation on Large Language Models!

[@ashleygesty7671]

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

[@Bhuvana2020]

What a fantastic tutorial! Thank you! Liked and subscribed!

[@kahoku451]

Incredible. Thank you

[@davhung8004]

Seems really really cool

[@dragonwisp9810]

This video deserves more views.

[@jacobwilliams4182]

Great explanation of an advanced topic

[@darrin.jahnel]

Wow, what a fantastic explanation!

[@ammarlahori]

Absolutely brilliant..great examples

[@Larock-wu1uu]

This was incredibly good!

[@eriksteen84]

fantastic video, thank you!!!

[@angelatatum8642]

Easy to understand explanation of large language models 👍

[@quantphobia2944]

Simply amazing, so intuitive..omg subscribed

[@jwilliams8210]

Outstanding!

[@DavideVarvello]

wonderful, thank you so much for sharing

[@sumitarora5861]

Simply superb explanation

[@mbrochh82]

Wow. This is incredible!!

[@CUDA602]

Amazing Video!

[@perseusgeorgiadis7821]

This was actually amazing

[@ryusei323]

This is fantastic. Thank you for sharing.

[@RK-fr4qf]

Fantastic. Please teach more You are a legend.

[@pointblank3409]

this is gold, thanksss sir

[@rose8968]

Wow this is so impressive. I wonder how many ppl who are teaching genAi actually know these

[@KayYesYouTuber]

Great explanation. Thank you very much

[@user-vc6uk1eu8l]

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...

[@magunciero]

Really well explained!!