#1 Solved Example Back Propagation Algorithm Multi-Layer Perceptron Network by Dr. Mahesh Huddar

#1 Solved Example Back Propagation Algorithm Multi-Layer Perceptron Network Machine Learning by Dr. Mahesh Huddar
Back Propagation Algorithm: • Back Propagation Algor...
Derivation of Back Propagation Algorithm: • Derivation of Back Pro...
#1 Solved Example Back Propagation Algorithm: • #1 Solved Example Back...
#2 Solved Example Back Propagation Algorithm: • #2. Solved Example Bac...
#3 Solved Example Back Propagation Algorithm: • #3. Backpropagation So...
#4 Solved Example Back Propagation Algorithm: • Backpropagation Solved...
Back Propagation Algorithm with bipolar weights: • 16. Update weights usi...
Multi-Layer Perceptron LearningSolved Example: • Solved Example Multi-L...
Multi-Layer Perceptron Learning: • Multi-Layer Perceptron...
Gradient Descent Algorithm: • 2. Gradient Descent Al...
Gradient Descent and Delta Rule: • 1. Gradient Descent | ...
The following concepts are discussed:
______________________________
Solved Example Back Propagation Algorithm,
Back Propagation Algorithm Solved Example,
Back Propagation Algorithm,
Multi-Layer Perceptron Network,
Back Propagation Algorithm Machine Learning,
Back Propagation Algorithm Multi-Layer Perceptron Network
Derivation of Backpropagation algorithm: • Derivation of Back Pro...
Gradient Descent Algorithm: • 2. Gradient Descent Al...
Gradient Descent and Delta Rule: • 1. Gradient Descent | ...
Machine Learning - • Machine Learning
Big Data Analysis - • Big Data Analytics
Data Science and Machine Learning - Machine Learning - • Machine Learning
Python Tutorial - • Python Application Pro...
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[@akinyaman]

man l looked many bp explanations and most of them telling of rocket science stuff this is the easy and clear one ever, thanx for sharing

[@safayetsuzan1951]

Sir, you deserve a big thanks. My teacher gave me an assignment and i was searching for 2 days on youtube for weight calculation. But finally your video has done the work. It was really satisfying. Thank you sir.

[@MaheshHuddar]

Welcome
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[@BloodX03]

Damn sir, u are the best youtube teacher in AI.. Love youu ma sir

[@R41Ryan]

Thank you. I've been trying to implement a reinforcment algorithm from scratch. I understood everything except back propogation and every video on it that I've watched has always been vague until I saw this video. Good stuff!

[@MaheshHuddar]

Welcome
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[@joeystenbeck6697]

This makes the math very clear. I now know the math and have some intuition, so I hope to fully connect the two soon. Thanks for the great video!

[@MaheshHuddar]

Thank You
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[@shivamanand8019]

It looks like error in ∆wji notation you followed just opposite
@@MaheshHuddar

[@jarveyjaguar4395]

I have an Exam in 2 days and your videos just saved me from failing this module.
Thank you so much and much love from 🇩🇿🇩🇿.

[@MaheshHuddar]

Welcome
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[@shreyaankrao968]

I got crystal clear understanding of this concept only because of you sir. The flow of video is excellant, appreciate your efforts!! Thank you and keep up the good work !!

[@MaheshHuddar]

Welcome
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[@tejasvinnarayan2887]

Very clear! How about bias b? What is the formula in case we add a bias?

[@vnbalaji7225]

Simple lucid example illustrated. Please continue.

[@MaheshHuddar]

Thank You

[@khizarkhan2250]

Guys! Threshold or bias is a tunning parameter you can select something low like 0.01 , 0.02 or high like 0.2 to check error is getting low or not. I hope this will help you.

[@paul-pp1op]

Best video explanation on ANN back propagation. Many thanks sir

[@MaheshHuddar]

Thank You
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[@dydufjfbfnddhbxnddj83]

how did you update the weights of connections connecting input layer and the hidden layer?

[@sahmad120967]

Great sir, it is very clear example how to calculate ANN. Thanks, keep being productive

[@MaheshHuddar]

Thank You
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[@LongZzz]

Thank you so much, this video comes right after I feel bad at machine learning and wanna give up but now I think it is not so hard as I think

[@MaheshHuddar]

Welcome
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[@mehmetakifvardar]

Mr.Huddar, thanks a lot for perfect explanation. One thing though, how do I calculate the change of bias term for each neuron in my neural network?

[@sharanyas1565]

Very clear. Thanks for uploading this video.

[@MaheshHuddar]

Welcome
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[@princepritam71]

You taught Very good. Today is my exam. Your videos were really helpful. I hope I pass well without getting a backlog in this subject. 👍👍

[@MaheshHuddar]

Welcome
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[@lalladiva6097]

you are a life saver, thank you soooo much.

[@MaheshHuddar]

Thank You
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[@adilmughal2251]

Amazing stuff just to the point and clear.

[@MaheshHuddar]

Thank You
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[@sahanazbegam6913]

very clear thank you for the content

[@MaheshHuddar]

Welcome
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[@femiwilliam1830]

Good job, but why no account for the bias term before applying sigmoid function?

[@ajayofficial3706]

Thank you Sir, for each and every point explain it.

[@MaheshHuddar]

Welcome
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[@abu-yousuf]

Great work Dr. Mahesh. Thanks from Pakistan.

[@MaheshHuddar]

Welcome
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[@ushanandhini1942]

Easy to understand. Give next one is CNN

[@AbhishekSingh-up4rv]

Awesome explanation.
Thanks

[@MaheshHuddar]

Welcome
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[@tanvirahmed552]

Nice video, easily understood the topic, thank you

[@MaheshHuddar]

Welcome
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[@alperari9496]

I believe for hidden units, the w_kj in delta(j) formula should have been w_jk. Namely, other way around.

[@alperari9496]

And delta(w_ji) should have been delta(w_ij), again the other way around.

[@steveg906]

yeh i was about to comment the same thing

[@msatyabhaskarasrinivasacha5874]

It'd an awesome explanation sir....no words to thank you sir

[@MaheshHuddar]

You are most welcome
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[@madhusaggi]

Can you please do vedios on Cnn with mathematical concepts..your vedios are much useful and understandable.Thank you

[@arminmow]

You saved me you're a hero thank you

[@MaheshHuddar]

Welcome
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[@df_iulia_estera]

Awesome explanation! Thanks a lot!

[@MaheshHuddar]

Thank You
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[@25fpslagger81]

Thank you sir, all of you machine learning videos have helped us students a lot

[@MaheshHuddar]

Welcome
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[@hemanthd623]

@@MaheshHuddar Sir Can you solve problems on HMM , CNN pls

[@usmanyousaaf]

Last night !!
Today is exam well explain boss

[@ToriliaShine]

thank you! understood the concept smoothly with your video!

[@MaheshHuddar]

Thank You
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[@ToriliaShine]

@@MaheshHuddar did that, question though. for the forward pass, what about biases? they are values with their own weights right. were they just, not included for this example?

[@ToriliaShine]

ah nevermind, got it from your next video in this series lol

[@tlbtlb3950]

不错，老印！

[@bharatreddy972]

Thank you So much Sir.....This videos made us clear understanding of Machine learning all concepts...

[@MaheshHuddar]

Welcome
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[@ervincsengeri1840]

Köszönjük!

[@MaheshHuddar]

Welcome
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[@bagusk_awan]

Sir, thank you for this great video! It was really helpful. I appreciate the clear explanation

[@MaheshHuddar]

Glad it was helpful!
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[@mr.commonsense6645]

GOATED Explaning, mantab mantab

[@MissPiggyM976]

Very useful, thanks!

[@MaheshHuddar]

Welcome
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[@Ayesha_01257]

Very Well Explained ...Keep up the Good Work

[@MaheshHuddar]

Thank You
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[@a5a5aa37]

thanks a lot for your explanation!

[@MaheshHuddar]

Welcome
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[@flakysob]

Thank you so much! You saved me. I subscribed. Thanks

[@MaheshHuddar]

Welcome
Please do like share

[@mdnahidulislam13]

Clear explanation. recommanded...

[@MaheshHuddar]

Thank You
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[@NotLonely_Traveler]

Finally one that makes sense

[@MaheshHuddar]

Thank You
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[@usmanyousaaf]

Today is my exam again well explain sir

[@srisangeeth4131]

Concept is clear , i got confidence in this concept sir,thank you👍👍👍👍

[@MaheshHuddar]

Welcome
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[@srisangeeth4131]

@@MaheshHuddar sir can you provide videos in gaussian process in machine learning

[@ram-pc4wk]

how are you deriving deltaj formula, u can include derivations of sigmoid functions.

[@AnubhavApurva]

Thank you!

[@MaheshHuddar]

Welcome
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[@iwantpeace6535]

THANK YOU SIR , BIRILIANT INDIAN MIND

[@MaheshHuddar]

Welcome
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[@tatendamachikiche5535]

good staff thank u

[@MaheshHuddar]

Welcome
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[@SaiNath-cw7yn]

thank u sir

[@MaheshHuddar]

Welcome
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[@SHUBHAMKUMAR-cd7fs]

awesome.

[@MaheshHuddar]

Thanks!
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[@umakrishnamarineni3520]

thank you sir.

[@MaheshHuddar]

Most welcome
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[@sathviksrikanth7362]

Thanks a lot Sir!!!

[@MaheshHuddar]

Welcome
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[@iqramunir1468]

Thank you so much sir

[@MaheshHuddar]

Welcome
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[@priyaprabhu7101]

Nice Video sir.. where is the bias here

[@romankyrkalo9633]

Great video, easy to understand

[@MaheshHuddar]

Thank You
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[@AmnaCode]

Thanks for solution

[@MaheshHuddar]

Welcome
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[@AmnaCode]

@@MaheshHuddar sure. Thanks 😊

[@user-fl7bm8jc8o]

Thanks a lot sir 🙏🙏🙏🙏🙏🙏🙏

[@MaheshHuddar]

Most welcome
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[@imranimmu4714]

thank you

[@MaheshHuddar]

Welcome
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[@utkarshmangal6559]

you are a king sir. Thank you for saving me from my exam tommorow.

[@MaheshHuddar]

Welcome
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All the very best for your exams

[@makkingeverything6610]

thank you man

[@MaheshHuddar]

Welcome
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[@lakeshkumar1252]

thank you sir

[@MaheshHuddar]

Most welcome
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[@color2653]

Thank you

[@MaheshHuddar]

Welcome
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[@jacki8726]

Very helpful

[@MaheshHuddar]

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[@pranavgangapurkar195]

in one epoch how many times back propagation takes place?

[@ayushhmalikk]

youre a legend

[@MaheshHuddar]

Thank You
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[@TXS-xt6vj]

you are a legend

[@MaheshHuddar]

Welcome
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[@shubhampamecha9650]

And where is the b biased
There should be some constant also?

[@allahthemostmerciful2706]

Soooooo Good❤

[@MaheshHuddar]

Thank You
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[@tuccecintuglu404]

YOU ARE THE GBEST

[@arj1045]

well done sir

[@MaheshHuddar]

Thank You
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[@pritampatil4669]

What about the bias terms ?

[@oposicionine4074]

How do you update de weights if you have more input data?? In this case he only has 1 input, how do you do it with 2 inputs? Do you do the same twice?

[@animeclub8475]

everyone says "thank you", but only a few understand that this video is useless if there are more neurons on one layer. Those who say "thank you" do not even plan to make a neural network

[@seabiscuitthechallenger6899]

👍👍👍👍👍👍👍👍

[@pubuduchanna1736]

Thank you! This helped me a lot!

[@MaheshHuddar]

Welcome
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[@Cobfhd]

The formulars for delta W only work because of the nature of the actiation function right? if it is a hyperbolic tangent or RELU , the formulas change right?

[@steveg906]

yes

[@sowmiya_rocker]

Thanks for the video, sir. I have a doubt. How did you update weights without Gradient Descent (GD) or any other optimization technique, sir? Because I read in blogs that Networks don't get trained without GD and by only using backpropagation. In other words, my doubt is how does the calculation change if we also implemented GD in this? I'm a rookie; kindly guide me, sir.

[@adityachalla7677]

gradient descent has been used in this video while updating the weights. the change in weights is done through gradient descent. But here he has not written the derivative math.

[@rohanwarghade7111]

how he got ytarget = 0.5@@adityachalla7677

[@user-se2fq2ug2t]

good video

[@MaheshHuddar]

Thank You
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[@user-kj3uo4ry4h]

I have a question, what if we have a bias term and some bias weights. Do we need to account for those or it would be 0?

[@MaheshHuddar]

Yes you have to consider
Follow this video: th-cam.com/video/n2L1J5JYgUk/w-d-xo.html

[@justenjoy3744]

From which book u have taken problem from?

[@timebokka3579]

Can you explain this
The input of a neuran is 0.377
Output of the same neuran is 0.5932
How this happens
How does the 2.71^-0377 give the answer of 0.6867

[@satwik4823]

GODDD!!!!!!!!!!!!!!!!!!!

[@MaheshHuddar]

Thank You
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[@user-gi8yl6qh3l]

txs

[@MaheshHuddar]

Welcome
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[@jinkagamura2820]

I have a doubt. In many places, I have seen that the error calculation is done using the formula E = 1/2 (y - y*)^2, but you have calculated by using subtraction. Which is correct?

[@keertichauhan6221]

same doubt .. what is the correct method?

[@priyanshumohanty5261]

@@keertichauhan6221 I think there are different methods to compute the error. The one that this guy above has mentioned is mean square error. The one shown in the video is also correct, but using MSE or RMSE, are generally regarded as better measures.

[@chukwuemekaomerenna4396]

For a multiple data point, you use error function and for single output you make use of loss function.
Loss function is error = actual - target
Error function is 1/2(actual-target)square

[@chukwuemekaomerenna4396]

For a multiple data point, you use error function. Error function=1/2 summation(y actual - y target)^2.
For a single data point, you use the loss function. Loss function=
(y actual - y target)

[@sharanyas1565]

The errors are made to be positive by squaring.

[@muhtasirimran]

I have a confusion. We use ReLU on hidden layer and not sigmoid. Shouldn't we calculate hidden layer's activation using ReLU instead of sigmoid?

[@MaheshHuddar]

Yes you have to
Do calculation Based on activation function

[@muhtasirimran]

@Mahesh Huddar ik. You have used sigmoid function on the hidden layer. This will result in an error.

[@SourabhKumar-nr1yq]

🙏🙏🙏🙏🙏🙏

[@MaheshHuddar]

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[@amulyadevireddy5669]

What about the bias factor??

[@halihammer]

He did not add it to keep it simple i guess.
But you can add the bias by making it a third input. Then the method doesnt change you just have a new (constant) input for each layer. Normaly set to one 1 and the weight associated will act as your bias. since 1*biasWeight = biasWeight. I just added 1 to my input vector and generate an additionol weight for the weights matrix. But im also just learning an not sure if im 100% correct..

[@nhatpham5797]

hello, thanks for your lecture, please let me ask after we calculate Denta 5 in backpropagation, why don't we calculate new w35 and new w45 and then substitute in the formula Denta 3 and Denta 4 but have to use old w35 and w45, please reply, have a nice day

[@MaheshHuddar]

The error was due to old weights,hence update the weights using old weights

[@nhatpham5797]

@@MaheshHuddar Thank you so much!!

[@dhanushvcse4056]

Sir gradient descant video kocham fastta video upload pannuga

[@MaheshHuddar]

Videos on Gradient Decent:
th-cam.com/video/ktGm0WCoQOg/w-d-xo.html
th-cam.com/video/5hB4_8o34GU/w-d-xo.html
th-cam.com/video/ibKP0nIT7YU/w-d-xo.html

[@vaiebhavpatil2340]

based

[@MaheshHuddar]

Based...?

[@lakshsinghania]

sir, also each perceptron has bias with it right ?

[@MaheshHuddar]

Yes

[@MaheshHuddar]

Follow this video for bias: th-cam.com/video/n2L1J5JYgUk/w-d-xo.html

[@lakshsinghania]

Thank u sir a lot!! @@MaheshHuddar

[@kaavyashree6209]

Sir how to update bias in back propagation

[@MaheshHuddar]

Refer this video: th-cam.com/video/n2L1J5JYgUk/w-d-xo.html

[@BujjiNaveen995]

Great

[@MaheshHuddar]

Thank You
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[@BujjiNaveen995]

@@MaheshHuddar done 👍

[@vinayvictory8010]

At what stage we have to stop Forward and Backward pass unti Error becomes zero

[@muhtasirimran]

this is specified as epoch and must be given in a question. There is no defined condition to stop. It really depends on your needs.

[@redwanhossain7563]

what about bias

[@slimeminem7402]

It is assumed to be zero here

[@kulbhushansingh9131]

it will be a3, a4 and a5 instead of a1,a2,a3 while finding the forward pass

[@kulbhushansingh9131]

the notations of delta w are also wrong, this man fcukrd up the notations

[@kulbhushansingh9131]

but honestly teaching good

[@amitasharma5481]

Hi, can I contact you, I need to solve my assignment with 2d vector data with mixture model. i need to perform EBP. Can you please tell me how can I contact you.

[@1981Praveer]

#Mahesh Hunder, dnt we need bias, just curious

[@MaheshHuddar]

Follow this video
th-cam.com/video/n2L1J5JYgUk/w-d-xo.html

[@1981Praveer]

@@MaheshHuddar @6:31 min Just curious can you share the web page for these formulae ?

[@user-kg7kw7cg6t]

mahesh daale