Contact

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 tutorialI 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

http://www.linkedin.com/in/mhmazur

39 thoughts on “Contact

  1. iam hopeing you can help igg inc aka texas holdem poker deluex is useing bots to cheat its players i have tryed many times to get igg to remove them and nothing happens i spoke out on there fourm and two days later my acct was locked igg excuse i transfered chips that is a lie seems igg programers made one big mistake on there bot program if you go on poker deluex 3d you can see the bots plain as day there blue with f markes on them when i brought this to igg great service deparment they turned into bunch clams wont answer my emails i have screen shots at four differnt tables showing them

  2. A Step by Step Backpropagation Example – Matt Mazur

  3. 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

  4. 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.

  5. 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?

  6. 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.

  7. Best articles to learn deep learning | Learn for Master

  8. 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] ?

  9. 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.

  10. 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.

  11. 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.

  12. 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).

  13. 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)

  14. A New Adventure: I’m Taking the Leap to Focus on Preceden and Analytics Consulting – Matt Mazur

  15. Automating Facebook Ad Insights Reporting Using Fivetran and Looker – Matt Mazur

  16. I’m speaking at JOIN next week! – Matt Mazur

  17. 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 :)

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