Implementation of AND function using Perceptron Model

Machine Learning: • Machine Learning
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[@ThinkXAcademy]

Important Correction: Activation Function for this video is defined as: if y(in)> 0 : then f(y(in)) is 1 if y(in) = 0: then f(y(in)) is 0 if y(in) < 0: then f(y(in)) is -1

[@wfkpk]

​@@rohitchitte9155 he is defining the new activation function; he is not using the sigmoid function that we have seen in the other tutorials.

[@jaykumarpatil4548]

Chapri explanation

[@mango-strawberry]

​@@wfkpk Why not use sigmoid? Any reason for that?

[@fatemehiraf3998]

that was the best teaching of perceptron that I have ever seen. thank you so much.

[@150kanduladinesh3]

I think w_new=W_old+Learning_rate*(Expected value-Predicted value)*feature but u gave it directly expected value.

[@mango-strawberry]

right it should be true - predicted

[@gqtshapha5640]

Clear, informative explanation...I like it👍 I was wondering though, does the ordering of the input layer affect the overall turnout? In terms of shifts and overall adjustments

[@ThinkXAcademy]

do you mean ordering of nodes within input layer?

[@wfkpk]

In 2nd iteration table you have written x1=1,x2=1,t=-1 but it should be x1=1,x2=1,t=1.

[@RazaHussain96]

why?

[@slainiae]

Yes, you're correct. It's just a small mistake he made though and everything else should still be valid.

[@slainiae]

@@RazaHussain96 Because it's an AND function which means x1=1 AND x2=1 should give t=1 and not t=-1.

[@computervisionlab2119]

Hi great video, I have a question: why you don't use backward propagation algorithms ? and sigmoid function is 1/(1+e^-x), did you round this function ?

[@ThinkXAcademy]

Back Propagation algorithms are obviously useful but in this example it works without using this, but definitely for complex datasets we will require back propagation.

[@learner8550]

What is formula of Linear activation function? For using here.....

[@TheIntervurt]

The formula for Δw is a(target - output)xi, in this video your formula is atxi ?

[@jeetbhatt5986]

Same question!!

[@pranavarya2164]

why did u not use threshold

[@MuhammadIrfan-nf9pb]

Hi bro I think In the first iteration and last 4 input the values of Delta w1, delta w2 ,delta b , w1 ,w2 and b are not correct. My values are 1, 1 ,-1 ,2 , 2 and -2 respectively comming plz Guide me

[@ThinkXAcademy]

You might have made some mistake in calculations. Because it is correct.

[@maheshh4414]

Hi sir, for the inputs 1,1 we should get 1 only right. In the first iteration if we update the weights earlier then we will get Y=1.

[@ROBERTAGAROFANO-j3b]

Amazing explenation!!!!

[@sshahidmalik97]

sir, since u have used the activation fn which classifies the out either 0 or 1, so how did u get -1 as output (y)?

[@ThinkXAcademy]

output of y will be passed to activation function for classification

[@sshahidmalik97]

Yep that's what i said.. After passing it to activation fn the result should be either 0 or 1 according to the activation fn which u have taken.. But u are getting - 1 for some input.. Which can't be possible... If your activation fn would have classified the output to either 1 or - 1 then it was ok.. But for this act. fn that is not the case..

[@ThinkXAcademy]

it is correct i have rechecked the solution in the book...

[@anelemadonda6191]

@@ThinkXAcademy I think me and Shahid have the same question, your solution might be correct but the question here is "how" did you get to the solution, "how" did you end up with a -1, *how*? thanks for the video btw.

[@ThinkXAcademy]

Activation Function for this video is defined as: if input > 0 : then output is 1 if input = 0: then output is 0 if input < 0: then output is -1

[@ishag9787]

Sir , how will be the truth table for logical nor for bipolar inputs ? Is it done same way as binary just that we consider 0 to be -1 ?

[@ahmarhussain8720]

awesome explanation

[@ThinkXAcademy]

thanks✨

[@balidinesh5687]

in the second iteration for input 1 the target is -1 but I guess it should be 1 and will our perceptron performs it right???

[@ThinkXAcademy]

Try it that way if you are getting final values satisying the AND condition then it will be correct👍🏻

[@rohan8758]

@7:25, you didn't tell about value of sigmoid function in perceptron learning algo. , not even y = f(yin), How can you assume that we know it already..

[@ThinkXAcademy]

Because this video is part of the Machine Learning course and you skipped this tutorial of the course: kzbin.info/www/bejne/r6S0pqFui8eIo68

[@abhishekdubey9920]

How you write on screen its amazing pless tell.

[@rohan8758]

In 4th time of 1st iteration my w1=2, w2=2 & b(new)=-2. Please check it once, & correct me if i am wrong, Thank you

[@ThinkXAcademy]

Yes correct.

[@sasidharreddykatikam328]

What is the terminology for ALPA and T in this perceptron Model

[@smritidhurandhar232]

is it complsory to use formula to update the weights and bias? or can we assume it by our own

[@ThinkXAcademy]

yes formula is needed to update the weights and bias.

[@smritidhurandhar232]

@@ThinkXAcademy but in some videos and site i have seen that they have solved with using formula [ w;(new) = zv;(old) + ct.t:x; ]

[@smritidhurandhar232]

@@ThinkXAcademy is using this formula [w;(new) = zv;(old) + ct.t:x; ]is compulsory or not?

[@ThinkXAcademy]

it is important to use this formula because weight needs to be readjusted after each Iteration

[@palvaisaiprasanna4553]

How to compute iteration 2 and what are the conditions

[@ThinkXAcademy]

Compute the same way as we did in iteration 1 but take values of bias from first iteration

[@rajershimeesala2368]

thank you brooo the condition u used in this prob f(yin) is McCulloch Pitts bro

[@ThinkXAcademy]

welcome🤗

[@kingofshorekishore]

nicely explained.

[@ThinkXAcademy]

Thank you😃

[@radhasingh3549]

value of alpha will be same for both the iterations?

[@w.n.n.amadubashitha6751]

yes

[@RyeCA]

How dou you construct the line using the weights and bias?

[@ThinkXAcademy]

using the formula y = w.x + b

[@RyeCA]

@@ThinkXAcademy perfect, thanks!

[@srimannarayanaiyengar8914]

good explanation

[@ThinkXAcademy]

Thank you sir😄

[@anjalinair3006]

Thank you! It helped

[@ThinkXAcademy]

Keep Learning👨🏻‍🏫

[@ArjunNarula1122]

Helped a lot, Thanks!

[@ThinkXAcademy]

Share my videos to help my channel grow😊💯

[@shanmugamkavin8795]

how to interpret delta w

[@abhishekdubey9920]

Bro its change in wheight which we do get targeted output and reduce error during traning.

[@karenngumimiiyortsuun4829]

Thanks man 👍

[@ThinkXAcademy]

Thanks😄 Share it with other students to help others too💫

[@Vipul__panwar]

why are u make it so complex 😵‍💫

[@achaaisakyapodcast156]

Bhai jaldi jaldi bol liya kar

[@ThinkXAcademy]

playback speed 1.5x pr krke dekhle

[@achaaisakyapodcast156]

Wahi kiya bhai, par bata raha future k liye

[@ThinkXAcademy]

haan meri new videos mei maine fast krdi h speed

[@ThinkXAcademy]

btw thanks for feedback

[@mango-strawberry]

@@ThinkXAcademy bro ek update formula mene ye bhi dekha tha: w new = w old + alpha * t * xi. Where t is either 1 or -1. Use 2 when the class should be above the line and use -1 when class should be below the line. Please reply bro if you see this, i have exam today