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The Essential Main Ideas of Neural Networks

StatQuest with Josh Starmer 1 views

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Seriously, we need to file a petition to rename neural networks to fancy squiggle fitting machines Also, make the statquest the founder of it
Comment from : @harshabhat2935

Godmode teacher I have never seen something like this before Increadible
Comment from : @girtsosmanis7269

Is a node is called as a neuron of the neural network Or a dense layer is called as a neuron 😢
Comment from : @jeferson1556

Thankyou so much for combining fun and learning this beautifully and creatively! God Bless You🥰
Comment from : @vanshika_6855

Never enjoyed and understood NN so perfectly! WoW
Comment from : @smartriddles20

Hey Josh, when will your book on Illustrative guide to Neural network and AI be made available on Amazon India? Is this an option world wide?
Comment from : @tituln3374

The singing is unbelievably unfunny
Comment from : @i9339

Absolutely terrible contrived explanation
Comment from : @i9339

I think this channel is a blessing for me Why haven't i discovered earlier 😢
Comment from : @naheedray

i might be a little late to the party but i was wondering about the values between the nodes of the example Are they just randomly made numbers or is there something i'm missing? I rewatched the parts but i couldn't see how you knew it was gonna be -344 between Dosage [0] in the output nodepath
Comment from : @lootcrate3800

I have been banging my head against the wall on why all the neural network teachers out there want to keep it a secret of what goes on inside the layers and neurons Thank you!!!! for not making it obscure
Comment from : @e33or772

You're the best!!!
Comment from : @mostafaalkady6556

This is the best neural network tutorial in all history of all time, I have never ever seen such brilliance
Comment from : @mohammadyassen2750

Great Video … you are a wonderful teacher sir …thank you so very much ❤
Comment from : @brpawankumariyengar4227

Great content But the voices you make are irritating
Comment from : @shahab4804

This really suits my disrupted mind
Comment from : @KarimAbd-Elrazek

Big Fancy Squiggle Fitting Machines Nah I Rather call it Neural Network🤣🤣😂😂
Comment from : @umar_muhammad_yaree

Wow, finally someone decoded the black box I did a Master's in Data Science and still could never truly understand neural networks Thank you Josh
Comment from : @AkashKashyap-rc4nk

So much intuition, especially with the graphs Very cool!
Comment from : @maplecyrupx

Nice tutorial and very well explained as always @statquest Have one question though The screen at 16:22 shows x -130 and x 228on the connections leaving the hidden layer, but in reality they are changing y value Is there a reason why it is mentioned as x? Also the results from two connections are summed to get the final result In reality does this sum happen in another node? Does it have a special name like combiner or aggregator?
Comment from : @soumyadas4261

"Take me to your leader" is a classic
Comment from : @openyard

This is easy to understand and sooooo fun to watch I literally laughed out loud at the "BAM"s, hahaaaa you're so funny people around me might be thinking I'm watching some random Mr Beast videos
Comment from : @Before2030

This is the best way to visualize the theory of a machine learning algorithm Thank you Sir
Comment from : @prottoykumar8922

Thanks!
Comment from : @cmfraser

These videos have literally carried my Master's thesis please never stop doing God's work
Comment from : @violentionamv949

This is THE BEST explanation I ever heard way better compared to my expensive master lessons Thank you so much!
Comment from : @jiaxuanchen7958

Wait, is learning actually enjoyable and fun?!? This video felt like 2 seconds!!! And learned more from watching this than attending two 15hr lectures which felt like 4 hrs each Nice I love it here
Comment from : @cloutii

why do we scale the y axis to -13?
Comment from : @agilap9807

Thanks!
Comment from : @janchoutka

And just like that, I understand the basics of neural networks BAM!
Comment from : @janchoutka

so Neural Networks have nothing to do with how a human brain works! it is mainly mathematical logic and operations and data it is deceiving to see YouTube videos and the main picture is a human brain and a computer and says "Learn Neural Networks"
Comment from : @AnseloSilver

When are you releasing the book on neural network?
Comment from : @AkhilSharma-dj9rr

15:49 - 16:17 is my favorite piece in Youtube history
Comment from : @JinXing-j1l

Could there be a video explaining ResNet?
Comment from : @user-nd6ut4qe3u

Hasta el minuto 17:35 pensé que yo no entendía bien porque veía que las gráficas de las funciones de activación eran distintas a tus gráficas, hasta que explicaste que los pesos y sesgos "giran y estiran" las gráficas 🙂
Comment from : @felipela2227

I'm so glad I found this gem You actually provided the explanation that I have been seeking The internet is flooded with people trying to explain neural networks, who have no idea themseleves but just parrot other peoples PowerPoint slides
Comment from : @lexxynubbers

Sir, you're helping me understand what I thought would take me years of study in just a couple of videos Thank you for this and for helping me get it done in my modeling course :D
Comment from : @davidbarrero525

Amazing video!! I fully understand Neural Networks
Comment from : @charlesmay1610

finally have found the best video format i can only wish for learning DS, BAAAM!!
Comment from : @malikau917

❤
Comment from : @Jagentic

thanks!
Comment from : @GiPa98

BFSFM !!!
Comment from : @Rupzx

Could you share which loss function and which optimizer did you use to get the values on your example, please? I know it's not the topic of this video, but i'm trying to get the same result as you, but can't Thanks for the help
Comment from : @gcedism

i thought you have to use a polynomial with unkown coefficient to fit the data Turns out there is no polynomial in the example😮
Comment from : @tsunningwah3471

Doesn´t neural networks overfit the training data?
Comment from : @andyn6053

I have a small doubt! brDuring calculting the orange values, from the second input layer, through the activation function, why the red box inside the orange box is different than red box inside the blue box? brDoes the difference in red box dimensions represent something? brI believe they should be some since both represents that we are taking y-coordinates of activation function
Comment from : @akankshaaggarwal394

THE GREATEST VIDEO OF ALL TIME!!!
Comment from : @akankshaaggarwal394

Your videos are great! But for neurl networks I liked 3BLue1Brown videos best
Comment from : @noadsensehere9195

We need full length music of badapbadaaboop
Comment from : @kartikmalladi1918

Masters Student here, went for my first NLP class and didnt understand a word After Class, started watching the series, Now I can understand what my prof says in class :)
Comment from : @peaky9072

Valeu!
Comment from : @Pedroswswsw

Thank you so much! I finally understand Neural Networks!
Comment from : @analuciamarzocca3748

Last part in your book MEGA BAMM Thank you for all the valuable lessonsbrBtw Great Book🥰🥰🥰
Comment from : @myronlewis06

i cried
Comment from : @tsunningwah3471

BAM!
Comment from : @gabrielsantos19

16:24 A dosage of 5 will be a little TOO effective, if you know what I mean :^)
Comment from : @TheHKEO

Yaaaaay! Thanks my Lord for this video series
Comment from : @frnab71

incredible!
Comment from : @Euglerio

You're a legend, Josh!
Comment from : @Irades

15:40
Comment from : @BarDots315

And by weightage can we say as slope
Comment from : @engrwajidkhan9603

Sir why we add only y coordinates of both curve? Because of considering as output?
Comment from : @engrwajidkhan9603

Petition to rename Neural Networks to Big Fancy Squiggle Machines! Great video Professor Josh!
Comment from : @ace2825

Waow! This tutorial was an amazing asset for revising my conceptsbrAlso, I haven't seen such an amazing teacher who clarifies these messy concepts, and makes easy to understand for usbrThanks a lot Sir Jost Starmer 🙂
Comment from : @syedmustahsan4888

Thank's a lot for an amazing explanation
Comment from : @hopelesssuprem1867

I'm a new data scientist who recently changed careers from clinical psychology and by God, I wish I knew about your channel and your book much sooner! You sir are a godsend
Comment from : @JosephCapelli

can someone please explain, why does the nodes correspond only to “red-box” coordinates of x and how did he managed to make such correspondences
Comment from : @teckdry

always grateful
Comment from : @nirajandas2268

Hi Josh, I am currently learning a lot about Neural Network and came across an litle error in your Graphical illustration and explanation of the Neural Network because the Output Layer also does the Activation Function but it wasnt mentioned once so i got very confused when i watched this series to deepen my knowledge of Neural Networks
Comment from : @minecoder5461

You made laugh, get interest and BAAMM! I'm studying a course of Machine Learning and got a profound level of stats, so I needed a couple of videos of depth and I found you This channel is amazing, I'm gonna watch everything You're awesome
Comment from : @guidolandinidrums

This is mind blowing Thank you brI have a question This is like overfitting the data with a non linear model Why can't we just find a non linear equation with the given data using some algorithm? Why do we need a neural network? Also how in this case overfitting generalises on unseen test data?
Comment from : @gurumoorthysivakolunthu9878

Unfortunately, neural networks dont fit as you call them”squiggles”, they fit groups of straight lines! brSVM algorithm is the one with the squiggles! So, now I cannot watch the rest, because I know it is a bunch of baloney
Comment from : @siavashsabet2462

LOL "oh no it's the dreaded terminology alert"
Comment from : @handlerhandle123

I really love the name big fancy suqiggle machine!
Comment from : @JohnZhang-hy2wj

Josh I just wanted to say thank you very much for all this content, it's really enlightning and very powerful in a sense that just with simple and not so fancy explanations I'm able to nail down every single concept and idea I'm currently taking a financial engineering masters degree and let me tell you that your approach has been really helping me out Also, in form of gratitude, I bought the 'Everything Bundle' from gumroad and it's been quite an awesome experience to learn from it and sharpen some loose math and stat concepts that I had on me Big thank you from Chile
Comment from : @Raifxtrader

I love you StatQuest, thank you very much!
Comment from : @rodrigoviana2444

each statquest video is better than the one before it easiest sub of my life right here
Comment from : @JohnBittner-iw3qy

Really works! I tried to demo and its working,i will try next to real account, this could be the best strategy i saw
Comment from : @HellaDepratt

Truly amazing! Thank you so much for everything you are doing!
Comment from : @gilao

thanks, really great explanation
Comment from : @hafizcaniago02

excellent videos You have a knack of keeping your audience hooked
Comment from : @mansisethi8127

Hello, Josh Your videos are really helpful for my research and I like them so much Btw, I really want to see you to explain mathematics theories and terminology behind Time Series Data Analysis, such as ARIMA, SARIMA or some other machine learning algorithms! BAM me! Thank you! (also from a model family fans, I guess you are too :)
Comment from : @JunQiu-pi1ky

Love this!
Comment from : @JaydenLin-y2n

I had seen your video about transformers and it was very impressive, so i decided tu watch others videos to review some concepts Muy bueno tu canal la verdad, minimalista, claro y con entendimiento profundo, me encanta Saludos
Comment from : @robertoort1011

great explaination with outstanding graphical representations and very funny presentation style ;) thank you very much!
Comment from : @Nodgelol1

I've been struggling to understand neural networks until i stumbled upon this video This is the best explanation with the best presentation (I agree fully on using easy to understand visualization instead of those fancy one) I don't usually write comments, but I feel the need to thank you for this Thank you so much!
Comment from : @jacobamarjan2325

Best one
Comment from : @ericgao9978

I wish I found this earlier, this makes me so into complicated machine learning stuff! Thank you so much and stay healthy!
Comment from : @hotrunghieu3268