Neural Networks Explained - Machine Learning Tutorial for Beginners

[@grandexandi]

I hope some day they invent a neural network that is able to explain in a simple way what neural networks are. We're obviously not there yet.

[@learncodeacademy]

Oh man, I know what you mean. I personally re-recorded this video at least 4 times in effort to make it simpler and simpler. Still not where I want it to be yet. The simplest description I have doesn't explain the "how", but more of the "what" and it's this: "Just like you teach a baby a dog and a cat, you show them a picture of a dog and say 'dog', then show them a picture of a cat and say 'cat', and do that over and over until they start to know the difference...that's machine learning - you show the computer some data and tell it what the outcome is, then show it different data and tell the outcome...over and over thousands of times until the computer tries all sorts of adjustments and can eventually sort out how the data affects the outcome."

[@1abbady]

kzbin.info/aero/PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So

[@austingadient2214]

Hey Alex. I recommend this article for something that is quick and easy to understand: youcodetoo.com/2019/07/16/what-is-machine-learning/

[@ArtOfTheProblem]

Here you go: kzbin.info/www/bejne/qGK4Z5mbo6yHqLc

[@navidrojiani6526]

Monads, too

[@Satanolgy]

This is exactly how I like to learn. Only a few people explain as clear as you. My professor just shows us math stuff that seem scary. Most professors are like that (screw them). Thank you so much!

[@praj8487]

This is a great tutorial for those who have some background in what a neural network is, have some knowledge of the technical terms used and are aware of the overall objective of a neural network. For absolute beginners, this may not be the best place to start. Perhaps you can make a tutorial on what a neural network does. Like how a marketing guy would pitch it to a non technical client?

[@angryazules]

tysm!! ive been looking at other tutorials and started to feel discouraged because i did'ntt understand the machine learning lingo and math behind it. This simple explaination is a lifesaver !

[@Etrancical]

I have read into this and watched several videos to help explain this really intricate topic. However, the only part I still really struggle in is, “Why do the equations work?” Anyone can copy an equation, but to make something of your own, you have to understand it.

[@xapd421]

Very true

[@beartikal]

Very math-free explanation and it is very helpful to pick up the basics. Nice job!

[@IamPali2024]

This is awesome. Finally a simple yet coherent explanation.

[@dheerajsn4294]

Probably the best tutorial on ML on the internet.

[@herbertduncan5838]

Best explanation I’ve seen. Thank you

[@AlexVoxel]

Thank you for the explanation! Finally something i can easily understand

[@iklimbov1]

Omg, you actually made me giggle with your explanations. I little bit confusing especially about back propagation. But, still very useful, thank you!

[@kitgary]

This is a great video! Neural networks is easier than I think, the most frustrating part is the maths but you probably don't need it as all ML frameworks like Tensorflow has done the complex maths for you.

[@nabiltech1366]

Yes btw we still need to understand maths behind them.Thats is an advantages for us to understand more about our NN

[@casseypaputungan3217]

Oh my God, this is the most helpful video in 2021

[@forte9300]

Ok. So after you apply the activation function to the sum. What then do you do to it? The arrows on the right arnt explained.

[@seantheisen7963]

This is actually really cool, its a bit difficult at first but once you get it you will like math X10 more

[@goutham2433]

A question. We give weights and biases to only first hidden layer or for all layers ? EDIT: After watching next video in series I understood that weights/biases are given to all nodes in all hidden layers initially. best explanation ever on this topic.

[@techwizpc4484]

where do you apply the calculated change? It didn't affect the weights and biases at all in the diagram.

[@nellynelly7551]

That's the new weight for the next iteration.

[@gihan121]

THANK YOU! After years (on and off!) of trying to wrap my head around this algorithm, it finally is "intuitive" enough for me to move to the next level and actually try and code one!

[@inkanahkala2149]

These are my first days of learning about machine learning and AI myself. It's "a little" confusing, but hopefully I'll get better and things will get easier in time. Especially when English is not my native language. I wish good luck to yall trying to figure out with these things, you are not alone.

[@Maeesha180]

This is what I really needed before dive dig into deeper! Thank you so much for such a nice explanation !!!

[@dylanloh5327]

Wow what u explained total nailed it! I been so confused of what neural network is and was only know its something u put in and it gives a output

[@nahidakhter8646]

beautifully explained! I've never come across such a simple and understandable explanation of neural networks!

[@doctor9101]

Finally the video I was searching for , u r tooo good.

[@hoodwatchurr4081]

After the activation function, do you multiply it the same way as before

[@Jcknight7996]

Would you have to have a "black box" or could you just have were the outputs and input are directly connected without a weight? Is there a way to train the bot programming without a weight?

[@shulihu6523]

the best explanation of neural network, thanks!

[@pritamaries]

Thank You sir. Really appreciate the effort. I hunted for a good explanation and finally i ended up with this gr8 explanation. Couldn't be happier.

[@nahbruhwhat]

Great overview and good vibes!

[@MrGN-yy6op]

ok we multiplied weights and added bias... what do we do to that? do we make it next weight of connection? or what do we do

[@hodlwise2470]

What are you adding some slight reverb to your vocal track? The acoustics sound great like you're on stage, not in a bedroom in front of a computer lol.

[@davidromero7823]

your video was very useful to clarify all my information about NN, thanks dude

[@primodernious]

actuallly all the nodes are individual perceptrons with inputs weights and biases. the activation of the output is just pushing buttons instead of feed forward. the network is learning by trial and error by mainy shifting the weight toward bolean yes or no like +1 or -1. the biased is just a way to overcome adding 0 with 0. the back propagation is just a attempt to speed up the result as you could just randomize the weigt over and over and try sum again and again until you get lucky. anyway in a game its the game rules itself that is supervising the network. technically a single perceptron can solve a whole stream of data point but since only one node have very little memory, a network is needed to hold the memory of the game as the neural net is just a array of variables at the core. its the wiring of these connections create a pattern from a to b that reflect where you are in the game. i mean there is a cronological order to the arangement of data in it that reflect the game itself. all the numbers in the network become similar to each other and are fractal like by default.

[@اسدخان-ه2ه]

At time 1:04 you said, "if you have seen my last 2 videos on Machine learning", can you supply their links here? I have not found them in your channel archive. Thanks

[@e-bikerides4618]

And who tells if the outcome is correct or not and how?

[@AnimeDR]

Ayeee, thank you for scrunching all this heavy content information in a way that makes sense. Props to you!

[@sanchitverma2892]

*NO ANIME*

[@mollytaylor8122]

At 10:30, what is delta? He says it's the "difference", but the difference of what?

[@kotopult]

Ok, I’ll learn ML :D That is amazing how interesteting and simple your explanations are for such complicated topic.

[@learncodeacademy]

Thanks! I actually took a month or two to try to get the explanation down...Neural Networks are simple, but complex to explain.

[@reuben4721]

yeah... very simple

[@robin212212]

During back propagation, are all the weights and biases adjusted at the same time, of is it one at time, recalc; then the next one, etc. ?

[@lucavisconti1872]

once I built a trained NN with my sample data, how can I use it with new inputs to get the outputs? thanks

[@calvinscarvings.66]

Just remove the improving code and add in the inputs to get the answer

[@davidstigg9355]

Great video! Thanks. I just spotted one mistake at 03:55 - you say "We've chosen three hidden layers." Do you mean "We've chosen three nodes in our hidden layer"?

[@ominous450]

I came to this video first. Watched it twice. Was still confused. Went on to watch 5 different other vids on NN’s and 2 vids on CNN’s. Revisited this video. Now it makes sense

[@simonkalu]

share those video links that you watched with us so we can all be on the same page.

[@DavidMauas]

🤯 OH... W.O.W!! THANK YOU Sooo MUCH!! I knew it was simple. But you just made it soooo easy for me to understand! I was already like🤪 , "what!!? Ok, I guess I have no other choice but to go over the code of a neural network myself because all explanations are just... Garbage!" People try so often to sound smart when they explain things they forget that the smart teacher is the one who actually makes people understand. I salute you! This has definitely made me want to code one myself.

[@rafaelsantos3723]

Great video! Thanks for sharing!

[@slaloulin8289]

I think maybe you could give more detailed calculations and visualize it from each iterations so that viewers can understand how the calculation works

[@learncodeacademy]

I do that in the next video with an actual neural network and actual console logs of each calculation. kzbin.info/www/bejne/b4vWqoSQpNqCh5o

[@slaloulin8289]

now this is great! this is all I need, thanks. I think you should change the title from "Neural Networks Explained Pt 2 - Machine Learning Tutorial for Beginners" to "Neural Networks Explained - Machine Learning Tutorial for Beginners part 2" so that it shows in my youtube recommendation section.

[@FerMJy]

excellent video... but you came out short with the explanation of the calcs of backprop... no idea what delta is.... no idea what pastChange is... and you don't have to change just the values and the weight... you have to change the biases... so............. i hope this is in the next video... really good so far

[@secretsather]

Should the input weights be converted to a value between 0-1 first?

[@welcometohell3002]

i don't get activation functions. how does that work??

[@perceptroncodex2173]

Activation functions are non-linear equations which combine non-linearities with each of the calculated node values in order to create a non-linear output. Why care about non-linear outputs? Well, because neural networks were designed to solve non-linear problems.

[@josephayooladele7771]

This is amazing!

[@alive4metal731]

Explanation gets the general ideal across... but yeah, the black box remains just that.. a black box.

[@thamizhansudip6644]

the bias value is common for each hidden layer. its not like for each neuron in the hidden layer will have different bias value ..... Pl correct me if am wwrong

[@dippy9119]

Great explanation thank you

[@dinoeld3800]

Why does every neuron have a different bias? Isn't the bias common for all neurons of a single layer? Or is this a different type on NN than the one explained by 3Blue1Brown?

[@forte9300]

Thanks for the equations by the way, huge help!

[@backflipbro790]

I'm still a beginner, but I dont think you need to know (at least now) why it works just like most people don't know why x = (-b+-sqrt(b^2(4)(a)(a)))/2a works (quadratic formula).

[@bhaskargovindaraj6942]

Mouse used can be bigger and contrast to the screen. So what happens is something not able to follow the mouse when you explain. Thanks

[@Stripes9999]

Great intro thanks!

[@jholid6y]

Does the output needs to loop back to the input for feed back? feels like this is required for learning in general. Your diagram doesn’t show this.

[@salmannotkhan]

Can you suggest me some basic recommend knowledge?

[@salmannotkhan]

@John Doe thanks for your suggestion

[@salmannotkhan]

@John Doe thanks again

[@xxsamperrinxx3993]

Hey what does the * value refer to in your change formula

[@FerMJy]

look at your keyboard.... it's multiplication

[@xxsamperrinxx3993]

@@FerMJy im talking about what 'value' is referring to but nice try

[@FerMJy]

@@xxsamperrinxx3993 change "formula" is for neuron's (or Synapse)Weight... sorry for the previous response in backprop.... think that you have to change - weights - biases - and.. you have to calculate delta(which is the most difficult to find info of, because lazy people don't want to explain the cycle of derivatives)

[@xxsamperrinxx3993]

@@FerMJy no like literally what does VALUE mean

[@DaveFanch1022]

@@xxsamperrinxx3993 believe value refers to the actual value from the output. 1 if it's a whatsit, 0 if not, for example. for the whatsit output node if you have a prediction of 0.35, delta would be 0.65 and value would be 1. for the whosit output node if you have a prediction of 0.96, your delta would be 0.96 and the actual value 0

[@atomiic39]

4:35 bros intrusive thoughts started kicking in 💀☠ literally everything else bounced off my brain

[@unamattina6023]

hey, what is the sort of these things? like machine learning, ai, deep learning etc. where should i start? could someone write basis to advanced?

[@govindbagla3599]

Plz more video in machine learning with JavaScript

[@bitonic589]

bruh what would the video be about.. tutorial on how to make a network in JS? 1. just use a library 2. or use a library 3. maybe use a library

[@Nova-Rift]

Were those numbers from the first calculation correct? I got different numbers than .3512 and .7891

[@urstte]

there is something i want to know, after that the learning is complete and we have an error lvl that is very low, lets say 1 in a million and there pops an error on the output how do we include that bias and we have already stopped learning, what i am saying might not be clear but what if the error is crucial and so far it is seeming to be impossible to have an error 0 lvl and the data that is in play is endless do we have to compare the human error rate to our network and move on or what do we do next?. i am starting to learn neural network and i want an answer if it is possible. also if we implement this on a real case that is out there like a self flying jet that has an endless play of nodes, is the simulation of this case or any other considered to be credible in terrms of laws and ect

[@RoyalBengalCub]

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[@KennTollens]

I keep finding how they work, but not how to actually use them. If I want a certain output, how do I set up my inputs?

[@websurfer5845]

Hey, when r u gonna upload the last part for vue and nodejs deployement?

[@MrGuardianX]

So how to correct the actual weights. That is the most interesting question. The rest in the video is easy.

[@Nedwin]

Thank you, this is helpful.

[@zx3215]

Ok, I watched your video, I think I may have now some idea of what NN's are... but I"m not sure. Give me more examples! (yes, I personally have a very slow learning rate) P.S. I'm just kidding) Thank you for the great tutorial)

[@ramingordi6260]

Thanks ... it was very useful

[@Edu888777]

Maybe you should explain what a perceptron is and why it need weights, what is Bias , than go deeper.

[@akinhwan]

crystal clear explanation. thanks

[@ab2595]

Hello, everybody. I'm Thanapol from Chulalongkhon University ,Thailand

[@jacksonzheng3103]

I wonder if we will ever be able to find the activation function and the hidden layer structure which our brains use to build better neural networks.

[@arandomguy46]

our brain doesn't use math to make decisions

[@jacksonzheng3103]

@@arandomguy46 nor does artificial neural networks. We just model them with mathematical functions. We've predicted the motion of subatomic particles with mathematical functions so what's stopping us from modelling the brain in the same way?

[@BULLSHXTYT]

Crystal clear

[@fernandoarnaldovilanculo8286]

Thank you!

[@raintzrandmaa9829]

Forgot about the biases, that's why I was getting some random noise instead of image.... Thx

[@StevenSmith68828]

If you really want to master this material program it only using numpy. Keras and other higher lever libraries black box what's going on. I'm on day 3 and I just got to programming back propagation but so far this has had the biggest effect on my understanding. I tried to follow many keras tutorial and have no idea what's going on besides on how to implement it.

[@spngled8654]

This is amazing

[@HarshRajAlwaysfree]

Well that escalated quickly you didnt explained what are these weight and biases , and what are their use :(

[@Etrancical]

Harsh Raj Always free I am not completely sure on this, but I think they are just a few randomly generated numbers less than one used to alter the first numbers in big ways. That way, if the answer is too low from the correct answer, you can change one of the biases or weights to make the end result more correct. And their use after training is so that they can be copied and pasted instantly instead of retraining the machine every time.

[@waelhassan1911]

The weight here is referred to the weight of the animal or the weight of the relations

[@Discoverortho1]

No clear explanation of the activation function and which one is chosen.

[@rabbitpiet7182]

Another issue with lower learning rates is local minima

[@radhakrishnanrathnachalam6980]

Thanks for the tutorial. Good. I have another question. Once the first animal's data done, the weights are changes by back propagation. But when the second animal data is passes, do the weights restored? Not clear from the tutorial. Thanks.

[@Psychentist]

Okay I feel like I can make one now, just as soon as I figure out how to sigmoid.

[@allisonscanlan4144]

Very good

[@silasmokone188]

what a video you have explained beautifully well done

[@amirrahman853]

is this easy or i am dumb enough to not understand that . bro i really wanted to know the concept of neural network and how to apply

[@evangofourth9213]

Seems simple enough. Now we just need n neural networks trained to identify and respond to different stimuli and we can retire as a species!

[@yashaswikulshreshtha1588]

I am learning how to learn to learn machine learning.

[@patchyst7577]

So far I understand, I just don't understand why you would name one of the input variables Weight since the connections are also called weight(s), could confuse people.

[@learncodeacademy]

Ha true, good point. It should have been lb, oz, or kg. I can see how that would be confusing

[@TMAB2003]

What is "whosit" and "whatsit"?

[@Edkeyz]

I think its an american slang for option a or option b as simple as that.

[@brickie9816]

I have just realized, that since ours brains are neuronal networks, this video trains neuronal networks how to train neuronal networks.

[@michelschlatter101]

I have created a web application that supports your learning process in the field of neural networks. blueneurons.ch/nn You can play around with the settings (architecture, activation functions, training settings) and observe how the settings affect the predictions. All datasets have preconfigured values that can be adopted. It is also possible to create your own datasets. Instructions and explanations to the implemented elements: blueneurons.ch/pdfs/userguide.pdf

[@doji-san]

WFT is a whosit and whatsit? You just confused me more!! :)

[@rfpeace]

this didn't help... Perhaps I need to go back and re-watch that first Neual Nnetwork vidoe again?

[@learncodeacademy]

It took me about 10+ exposures to neural network explanations to "get" how they work, so I'd say watch this video kzbin.info/www/bejne/b3ndZINni8x_sJY to understand how you USE one...then watch this video again, and pause as much as needed at each step. Also helpful, may be to watch other youtuber's videos, although, I did find most explanations out there very confusing and mathematically intense when learning.

[@war0nstupidity767]

Better go here kzbin.info This video is poorly scripted,

[@arunarkamukhopadhyay6443]

Thanks a lot...

[@yashaswikulshreshtha1588]

Him: it's not super complex Me: you mean it's not 100% complex Him: YESS! Me: SOoo the limit is 99.

[@exbladex99]

too number-filled explanation. The graphs/activation stuff you showed didn't actually help with the explanation. Why would I use sigmoid or tanh...? Felt like you skipped over backprop with bare mentions. I think your explanation made sense, it just hard to visualize for a beginner. Maybe you need code.

[@rey1242]

I also feel like that for almost any tutorial out there that "explains" backpropagation They just skip the presentation of the math "Oh yeah thats how we feed forward on the network... Yadda yadda yadda yadda... *Wastes 30 mins talking about something easy*... And that backpropagation... *Shows a few arrows pointing backwards the network for 5 seconds*... Thats it"

[@priyanks91]

Been watchin many videos but this is damn good !

[@Dave-lc3cd]

Great explanation but the whosit whatsit was a little confusing for me.

[@sakirdag2976]

thanks