How Does a Neural Network Work in 60 seconds? The BRAIN of an AI

Video quality	The size	Download

Information How Does a Neural Network Work in 60 seconds? The BRAIN of an AI

Title	:	How Does a Neural Network Work in 60 seconds? The BRAIN of an AI
Lasting	:	1.00
Date of publication	:
Views	:	186 rb

Full video on how the Brain of an AI works is here: youtube/NxTTXuUl-Lc
Comment from : @ArvinAsh

essentially you're teaching it whats right by making constant adjustments to its results? like showing them whats right and wrong
Comment from : @bluedream420z

the most important is the activation function
Comment from : @alliouasofiane

Forget the maths, just show the mechanics
Comment from : @JasonKing-m6m

But if we gave a structured data to a neural networks with proper datasets like a input layer including the hidden layers so the neural networks can understand the input layer and generates the output by understanding the right and proper parameters of the neurons and helps the Ai system also
Comment from : @arunkumarmahto8803

Arvin ash is one of my favourite science communicator Most underrated channel IMHO views should run into millions
Comment from : @amanvijayjindal5742

LLMs are basically guessing engines that guess the most likely response or series of words based on trillions of sentences from their training data They are a neat party trick that can simulate real intelligence to an extent, but are highly prone to bullshitting
Comment from : @ethaneveraldo

'Hooft used this math in his cellular automaton interpretation It is quite similar to Heisenberg's matrix mechanics(QM)
Comment from : @frun

missing activation function missing back propagation anyone that says this is a good explanation does not understand it at all lmao
Comment from : @austinmurphy9074

So like a controlled Pachinko machine?
Comment from : @SarcasticHole

🎉🎉🎉 awesome short
Comment from : @Atulipan

What about the activation function / value?
Comment from : @julioagueros

Nobody ever did that in elementary school 😅
Comment from : @HEARINGequalsBELIEVING

Everything in the end is math but not "just" math and the soul with the feelings aspect gives colours to things
Comment from : @mednowalid

Without activation functions, all you have is a linear function that is not enough to learn most interesting real-world functions that are nonlinear
Comment from : @aroundandround

Not accurate , it not just a mathematical equation
Comment from : @neerajsingh-xf3rp

Connect multiple neuron together to comprise NEURON network, NOT neural network Connect multiple computers together to comprise COMPUTER networks, not ComputationAL network
Comment from : @bernaridho

The full link isn’t working
Comment from : @interesting_stuffs

Some of the secrets lays in the backprogation
Comment from : @Ken_Adams80

No not like in the brain, a simple understanding of The Chinese Room thought experiment tells us it is impossible for digital computer to think feel or understand ANYTHING!
Comment from : @YTispartofproblem

Isn't it meant to be a Sigmoid function, not a linear equation?
Comment from : @neologicalgamer3437

Just gonna say that was not what my elementary school math looked like
Comment from : @n-7220

How does our brain figure out how to weigh and modify data? How did it first learn or figure out how to learn or figure out?
Comment from : @imaginaryuniverse632

There’s def a hessian in there for those who want to literally math out a simple perceptron
Comment from : @MyMattinthehat

Bro took algebra in elementary school?
Comment from : @aidanvega9778

❤
Comment from : @Mrpavan0

Damn, I must have been absent that day in elementary school
Comment from : @salamander554

does that mean we train the parameters(w,b) to get optimal result ??
Comment from : @kianx2093

Which means that the I in AI is misleading There is not the slightest bit of Intelligence in any AI system It’s just interesting and funny something to see what these massive mathematical systems can come up with
Comment from : @Blubb5000

So it's basically multilpe regression models
Comment from : @azaankaleem5521

That was the best high level explanation fast I have ever seen
Comment from : @Rackerintraining

You are one of the few who can explain complex subjects in the simplest possible way !!!
Comment from : @bhupendranath2010

Doesn't that mean we can use a value in a 2d sim as dynamic weights instead of static updates ?
Comment from : @way2on

We really never used bias in school 😅, just weights
Comment from : @aleksmarinic5748

all this stress to find out it’s just the fuckin slope formula????
Comment from : @AccordingToWillow

Thankyou 👏🏽
Comment from : @Outchange

You forgot the non linearity function
Comment from : @muhammadfaizanalibutt4602

Unfortunately this type of neural net has zero plasticity and cannot learn on its own That must change someday
Comment from : @mrspook4789

One of the best explanations
Comment from : @petermartnik

whater
Comment from : @verizonextron

Neural Networks that uses Negativity Bias are doomerbots
Comment from : @Masonicon

good explanation and great visuals BUT you are missing the importance of a neurons activation function herebrWithout it, the whole neural network basically shrinks down to a linear regression Adding an activation function turns the regression into something like a logistic regression A logistic regression with a verry hard cut basically is (mathematically) identical to a perceptron, which is the simplest form of a neuronbrAdding multiple of these together creates a multilayered perceptron (short: MLP) Big MLP are what we call 'artificial neural networks'
Comment from : @Anaeijon

I hate meth, i love math😅
Comment from : @warrenarnold

Are you saying my bias can be physically weighed? Cuz I surely have a heavy ass bias
Comment from : @PeaceNinja007

Is this the linear regression equation? Why not the sigmoid equation?
Comment from : @danielmoore4311

This explain nothing, the mul is a filter, the addition is the decibel measure, the bias is the threshold Basically low bias encode and logic, high bias encode or logic, so it encode a sliding logic 2 layers encode xor logic Therefore neural network encode three sets operation, discrimination, composition and equivalency
Comment from : @timmygilbert4102

Missing activation function Non linear is an important part of ANN You should not miss that
Comment from : @mlab3051

I can finally add AI into my CV
Comment from : @hdsz7738

Awesome
Comment from : @nasamind

I thought the function of a neuron was slightly more involved than this? I thought it was some kind of logarithmic response to the input not a purely linear function?
Comment from : @derekgeorgeandrews

What elementary school he went to ☠️
Comment from : @자시엘

I am now seeing the importance of my discrete mathematics class
Comment from : @ocean645

This is a very dumbed down explanation, i can understand wanting to avoid the linear algebra but making the dot product into multiplication between 2 variables and calling it "elementary math" seems extremely misleading as those 2 variables represent maybe thousands of values
Comment from : @Oscar-vs5yw

Simple equation from elementary school? I didn't learns functions in grade school
Comment from : @ancientheart2532

So it's fitting a linear model per factor?
Comment from : @kbee225

Ohno wonder😊
Comment from : @RIVERANIEVESZ

Very good explanation
Comment from : @ChathuraPerera

i substitute chatgpt for google searches in the future maybe it will be all chatgpt like softwares and companies will fight over the market of AI and the resource to fight with is the Data bec Ai needs data
Comment from : @arielpirante2

Stolen from @3blue1brown
Comment from : @adityapatil325

but don't most neutral networks use sigmoid, not linear, functions?
Comment from : @hoagie911

“Schematic of a simple artificial neural network”
Comment from : @YUSUFFAWWAZBINFADHLULLAHMoe

❤
Comment from : @FrancisGo

Mathematics usedbrto describe ANNbris very simple ❤
Comment from : @chenwilliam5176

nahh you gotta make a whole course on this you're too good at explaining
Comment from : @thymos6575

👏
Comment from : @tabasdezh

excep ha i involves marice mulipliscaion
Comment from : @jeevan88888

Those who are fooling themselves with this video just try to understand backpropagation😂😂😂 It is not simple at all
Comment from : @subhodeepmondal7937

There's like a thousand explanatory videos about how NN work and like none actually using it for anything * useful *
Comment from : @GregMatoga

He learned this equation in ELEMENTARY SCHOOL ???
Comment from : @OmniGuy

Thanks Very simply explained!
Comment from : @constantinvasiliev2065

Awesome simple explanation!
Comment from : @yashrajshah7766

But NO, our brains couldn't be working this way and we couldn't possibly be biological machinery right? The main difference is that we take 24 hours or so to create a newly trained network, and AI on silicon is millisecond or less for a new updated neural network The "math" of AI was able not only to learn chess better than any human in 9 hours, but able to beat the best human created program
Comment from : @snap-off5383