Back Propagation in Neural Network with an example
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- เผยแพร่เมื่อ 10 เม.ย. 2018
- understanding how the input flows to the output in back propagation neural network with the calculation of values in the network.
the example is taken from below link refer this mattmazur.com/2015/03/17/a-st... for full example
Dude, I can't thank you enough. You should teach teachers how to teach. Feels like I could never get a degree if it weren't for Indians. Every helpful video I can find, it's always you guys. Bless you.
💀
The details and clarity is so good 👍
he doesnt know what hes doing.
Very worst
I started watching this video muted. I was thinking that he explained how RNN's work,😉
Finally, someone who actually explains how to backpropagate!
Those videos are priceless if you are learning AI.
First we need to see it in biology
@@johncharalambous2488 not psychology logically
@@johncharalambous2488 yes its just a theory no need to des motivation or motivation just knowledge
@@johncharalambous2488 well what you men with chill
I was looking for something like this from long time, finally some one knows exactly what we need to know, someone provides us something different than others. thank you , thank you
Thanks for your explanation! This is the most quantitative example of backpropagation I have been searching for a long time. It truly helps me understand the mathematics behind this algorithm.
*Give this man a medal*
Great work.
This video is just what I needed! You're one of the only people... if not the only one... that went through the process with actual examples of inps and outs so that I could check my code each step of the way. THANKYOUUUU
You made a really good content. I have seen many animated illustrations and other articles as well, but this is by far the best. Very easy to understand. Adequate mixture of formulae and numerical values, to teach the content. Thank you so much for this wonderful work.
Thank you so much! Your video helped me clarify the backpropagation algorithm. Simple and elegant explanation.
OMG! you are amazing!
Wow, I have never seen teaching simple like that easy! Now I understand the base of Neural network! Especially in the hidden layer's processing. Thank you very, very much!
Yes, you are a genious. Your explanation is the best one on backward propagation I have ever come across. Thanks.
Honestly one of the best explanations out there for backpropagation, and for how the errors affect the network in general
This is awesome! After going through series of backpropagation videos, someone actually explains the concept with a clear example. Thumbs Up!
Mr. Naveen Kumar
Excellent job, could you add
1 - recursive neural network
2 - convolution neural network
3 -tensorflow
and anything related to NN you think is necessary.
Thanks very much Sir
Good Luck
Robert
Thank you, its a great and fast explanation of backpropagation. I was struggling with keeping every part of Backprop in order and your video really helped me be assured that my initial understanding was correct :)
Thank you so much for clearing everyone’s mind by doing a specific example. Beautifully done. That’s awesome.
It is epic! I know a lot of people have appreciated your work, I just wanna add a big THANK YOU.
Once we get into your pace and accent this is a very useful tutorial walking us through each step, I like your style, many thanks for your patience
Neveen thank you, the example is a great description of how a back propagation perceptron works. Among many videos I have seen today, this is the best one.
Helped me a lot when I was getting started - good stuff!
Ohh Man!!!!
I couldn't appreciate more than what others already appreciated you in below comments. You are hidden treasures. Hope that google shows up your videos in the first result.
You have put lot of effort in making this video & it is to the point!!
God bless you and keep posting! :)
Perfect Numerical Example for my tomorrow exam. Thanks, Buddy
Now I am in your position
@@beletetekle5593 Now I am in your position
@@notaladeen4156 best wishes
same position dude
Thanks. Extremely practical way of understanding how how the neural networks work. Good job
Thank you very much for this wonderful explaination !
I wish I saw it 6 months ago ! this is just perfect :(
Excellent Explanation! Thank you for all the effort that you have put in making this video!
even though I had a hard time understanding your accent, but you gave me the idea and I got your point, thanks
Finally, I understand something about backpropagation. Thank you so much for your mathematical explanation.
Great tutorial m8. The one thing that I could not find a clear explanation of was updating the weights and you did an excellent job at explaining it. Thanks for the video.
Going step by step through the back propagation was super helpful. Didn't miss a beat! Great explanation.
Very detailed and clear explanation. Thank you for your work!
Brilliantly done! I’m very grateful for this work that you are sharing in this wonderful video you have made. Thank you
This undoubtedly is a real quality video.
explained all that stuff so easily. other videos on youtube, trying to make the concept more intuitive, made them difficult to grasp.
thank you so much!!
Brilliantly, Simply and Clearly explained. Good job!
This is absolutely amazing. Thank you so much for this video. ❤️
Learned more from this video on NN than any other out there - thanks.
Thank You So much. Crisp and Clear explanation. I was not able to understand this concept earlier but you made it crystal clear for me. Great job!!!!
This was a great example, even the math was explained and how you substitute the values to go forwards and backwards. Great tutorial!!
Thanks for such a wonderful explanation Back Propagation in Neural Network with an example. It make the concept of Back Propagation very clear.
Dear Naveen, Thank you very much!I thank you for explaining every step. Just pen and paper with each step. Great
Thank you for this nice example. It's a good resource for explaining to students the fundamentals of forward and back propagation. I found your presentation including your english quite clear.
This video has helped me immensely to understand the concept of backpropagation. Kudos to you my friend! Also, could you explain the same with Matrices? It would be really great if you could. Thank you!
That's the video I needed. Thank you!
It would have taken me days to learn this. Thank you Naveen, you are amazing
This was a great example and explanation! really great job at explaining this man! thank you!
Finally got someone who explained well, very very awesome video
Nicely done! Thanks for explaining this in detail!
I heartly appreciate the efforts that you put in this vedio especially those solved examples , Thanks !
Wonderful! Easy to understand.
Thank you. Simple and effective.
thanks for the video! the concept is clearly explained and simplified enough to understand. There can be improvement on delivery.
Fantastic video, really clear. I search a lot for something such clear as your video! Thanks
one of the best clarification of BFNN .thanks alot
Thank you so much for explaining it very clearly!!
BEST EXPLAINATION I"VE been trying to get a hold of the idea and never really grasp it untill I saw this!
It's incredible. He was the first one who actually showed me each step with examples and not just formulas no one can read
Crystal Music exactly
@@Sh1r449 Are you still active in artificial intelligence ?
So i finally understood how the algorithm works!
Thankyou so much! Keep up the good work.
bro can you tell me why out H1 and H1 are different . In Lectures of Andrew Ng Out H1 and H1 are same
@@krishnanarwani8105 No, even in Andrew Ng's course it is not same. there's a slight difference between them. H1 is calculated by summation of products of previous activation with weights, i.e H1 = x1*w1 + x2*w2 + ... so on.
While out H1 i.e activation of H1 is calculated by passing the answers (result H1) through sigmoid function.
So, activation of H1 or out H1 = sigmoid(H1)
So, both are different. Hope it helps!
@@shamazafar8127 yes you are right brother
@@krishnanarwani8105 ya i am a girl btw! :D
@@shamazafar8127 ok😂
Thanks for this lecture. It really helped me a lot. Before watching this video, I almost gave up understanding just the concept of the chain rule. It seemed like one of those mysteries around the world such as unicorn or things like that lol. Once again, thanks a lot. You'll never imagine how relieved I am right now after finding this lecture. Have a nice day ! :)
One of the best explanations so far
At last, I understood the concept of Back Propagation after wasting lots of time.
Excellent job, hat's off and really you are a genius.
Keep it up.
he copied it form a website but this is more easy ot digest.
best video i found so far. thanks!
Thank you so much, very clear explanation
SO nicely and clearly explained, many thanks Sir!!
The best Neural network explanation video. Thank you so much :)
Best video about backpropagation out there
Tottaly agree
Great explanation sir one thing worth mentioning. If you use the squared error function to determine your error you will run into issues when you try to optimize your parameters, you will have multiple local minimal and no convergence to a single optimal value. you should probably use - 1/n Sigma target* log output + (1 - target)* log(1-output)
Best and simple explanation ever. Thank you so much dude ❤️
This is the best explanation i have ever seen for back propogation.
Very practical approach! Thank you :)
Dude, you are PURE GEM. cant thank you enough.
Nice calculations. I like your work, keep it up.
Really superb! even after doing a lot of courses, I could not understand. But after your explanation, I understood. Thanks a lot
Thank you for the wonderful explanation.
Best example I have ever seen on this topic. I cannnot wait to thank you. You are the Star*
this guy taught me what my teachers couldn't in whole f*cking semester and that too within 13 minutes . tons of respect to you mann!! may god bless you
You are an excellent teacher sir, thank you.
Great job explaining bits and bytes of calculations!
Awesome video, it was all numbers and formulas untill i watched this.Everything fall into place.Thanks!
Thankyou very much bro, you explained updating the weights in a neural network really really well !!!🙏🏽🙏🏽🙏🏽
This video is priceless ! cant thank you enough
So easy to understand. Thank you so much
Simple and Great, Many Thanks
Thank you for this great video 🙏
Fantastic video. Thanks for your time and effort. You made my day.
Perfect explanation!
You made my life easy!
Excellent job! Thankyou
Thank you so much! It really helped me out
Thank you so much. Very useful!
best video on net for back propagation. much better than all those big names from harvard, stanford, MIT etc.
This video is a gem
Best expalanation for backward propagation..Thank you.
This is one of the amazing videos. Although I am not a very technical or statistical person this video cleared my few concepts about the Neural network - backpropagation and feedforward part and how to calculate the errors and update the weight to find the target value. I am hoping to see how to adjust the biases and variances to get efficiency in my model. Please explain those as well.
Thank you, this video is very clear
immersive, intuitive, answering exact questions coming up in mind before asking, on the train of thoughts completely. appreciated it, dude! the only imperfection is I couldn't follow your accent pretty well, but you know what, amazingly - even though I couldn't manage to understand completely what you're saying I understand what you're saying - you know how neuron work, you know more how the human brains work. brilliant!
This video is the best I've seen on youtube. Whiners would benefit from using the pause button and going back as many times as necessary. All the information is there and is thoroughly explained. It's a complicated subject and it's unrealistic to "get it" right away.
Well Explained. Thanks!
Awesome thank you so much.. i was stuck at one point which you mentioned
Excellent video :) Thanks
Well explained :) Thank you. It helped me.
Very nice explanation !!! Thank you
Thanks, finally I have understand this algorithm