Hey there!

There are a few easy ways you can reach me if you ever need anything or just want to say hi.

**Email**

matthew.h.mazur [at] gmail.com

Note that due to the quantity of emails I receive about neural networks thanks to this tutorial, **I am not able to help with backpropagation questions** – sorry about that! I’d recommend anyone wanting to learn more to check out PyImageSearch.com.

**Twitter**

@mhmazur

**Skype**

m.h.mazur

**LinkedIn**

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A Step by Step Backpropagation Example – Matt Mazur

Thank you so much, I can’t understand back propagation algorithm before read your article.

I seriously can’t thank you enough for the step by step backprop explanation! Easy to follow and you didn’t skip steps. This has helped so much with my AI class. Thank you

Thanks ,It was a very clear explanation of back propagation .

Thank you so much for the detailed explanation on back propagation

I spent entire day doing what you did on paper myself. It was an awesome day since you inspired me. Appreciate your brave and kind efforts.

Very good explanation. Thanks.

hi, Sir I have read your article and I got an understanding on artificial neural network and it is really valuable article but what I have a question is how we can train if the input is not in binary or if it is an ASCII character?

Doubt: do we need to update biasing

Thanks! This explains it very well! A couple things that would be neat to see as improvements would be to alter your java program you use to show the neural net in action and have it be a bit more interactive. By this I mean be able to step through say the first handful of calculations or just be able to stop it at will and then have it display the equations with real numbers as you click or mouse over the different parts of the model.

You are a great teacher!

how to determin the initial weight and bais dependend on the actual input and output

Hi – how can I update my comments, I dont see an option.please let me know.

i just have one question about biases.

bias’s weight doesn’t change?

The tutorials I read when writing this didn’t update it so I didn’t here. It is something you can add on though.

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Thanks Mazur, “A Step by Step Backpropagation Example” is very well written compared to many other similar ones found in the net. I have a question, lets say the target output values are ’12’ and ’78’. Should I normalized these values to [0 ,1] ? If not, how do I calculate the output error where the training result is in range [0,1] ?

It would be even better if you could talk about the iterations

I mean you just took 1 training example

Please explain how to iterate all examples in dataset and epochs.

sir i need your help in my final year project……….

I am going to join the others on here and say thanks for great explanation of back propagation!

I don’t know how will I thanks to you for this excellent step by step explanation for the Backpropagation algorithm . Its a excellent work .and helpful for future work.

Concerning the backpropagation example, it was great for me to understand it. However, it doesn’t seem to mention the change of the biases in the process. Moreover, the image shows that the same bias is used on all neurons in the same layer which confused me a bit since each neuron usually has its own bias. It would be nice if you manage to modify the article based on these two ideas.

Very nice! I’ve used it to check my own implementation in Max/MSP and Python. However, after the first iteration my MSE equals 0.291027773694 instead of your 0.291027924. If I use the exact weight representations as in your example my MSE becomes 0.291027773717. Can’t exactly figure out what causes the difference(s).

thanks you are great i confused before know is better so please tell me where can i dowload for this exciting example

Example Max/MSP patcher using the included Mac 64-bit externals as attached.

Mac external sources on GitHub: https://github.com/mdehoogh/max_nn.git

Best,

Marc de Hoogh

Delft University of Technology, The Netherlands

Thank you for the explanation of MLP.

This is great :) Thank you!

This is great :) Thank you Matt!

Thank you ! about “step by step backprop explanation”!

Thanks for great example, but I have a question. When you calculated a partial derivative dEtotal/dOut1 you’ve got in the result a (-1) multiplier. Why?

(x^n)’ = n x^(n-1)

Hi, a small typo in net-h1, it should be 0.25*0.1, instead of 0.2 * 0.1

Hai…

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Nice bro…and very helpful

look like you work is copied

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Thank you so much for your great article :). one question left, what will be the backpropagation look like when we do dropout? especially I was reading this paper https://arxiv.org/pdf/1705.02033.pdf which is a very interesting paper, but Im not able to figure out the backpropagation regarding their approach!. it says that for their approach in feedforward step, they will keep the highest neurons and give other neurons zero, but sum up their activation and add up to the highest neuron. it means the activation of the highest neuron will be Z=0.8+alpha* 0.3, which 0.8 is the activation of the highest neuron and 0.3 is the summation of the other neurons. then in backpropagation, this 0.3 will be propagated to the neurons which they made inactive with alpha amplification. how it possible really? how the inactive neuron will participate only with alpha amplification to update the weight. do you mind have a look on the proposed method and share your idea?

Thanks :)

Excelente and useful article.

Really take better help… i am having one query: what do we say of error equation? is there any name ? please describe me.

A Step by Step Backpropagation Example - Jacob is studying on programming

Thanks for the note, it’s very well explained.

-I would suggest you take care the change in the weights for the bias.

-if you use the relu function instead of the sigmoid fuinction, the back prop number of iterations will fall down from 10K to 30.

relu(x)=0 if x<0 else x and

dRelu(x)/dx=0 if x<0 else 1

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