Discussion of article "Neural Networks Made Easy" - page 6
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An informative article.
When working with neural network forecasting, usually experimenters try to train the neural network with candlestick patterns, which does not lead to good results in real trading. I think it is more correct to try to train the neural network using different volume data.
Awesome! I was waiting for this a lot. Have wrote similar myslef but you have better quality! I would be happy to have this as part of MQL5 language with offical support from MetaQuoes team! We need to gave good AI core as part of thos platform - Ibhave created so many workaround to run with Tensorflow and to feed data in and out... so much better if all can be done from MQL5!!!!
In my opinion it is more realistic to ask MetaQuotes team to implement an easy interface between Keras and MQL5. However, I guess the current Python interface in MQL5 could be used to some extent to run Keras based models (at least for predictions).
Mr. Dmitriy,
Very interesting article, i really appreciate, Congratulations, but, could you please tell us how can we use these function classes in a basic model inside OnInit(), OnDeinit(), OnTick() and OnTimer()? How should i call your BPB NeuralNet Functions the right manner inside an EA code?! Let's suppose a 'XOR Problem', or even a basic structure with last ten ticks value being placed on input, so i can understand how it works, how to define quantity of hidden layers, where do i insert input's and recall the output?
I've been integrating via Python socket one BPB external code, but i think is reasonable to believe your code could be very effective directly inside my EA.
Thanks in advance!
Hi Dmitriy Gizlyk,
your article"Neural Network Made Easy" is great!
Could you give an example where you use your classes?
For example, a simple task such as: A neural network that can convert from binary numbers to decimal numbers?
The article is interesting! Thanks to the author!
I would like to continue with an example of neural network initialisation and its training.
Mr. Dmitriy,
Very interesting article, i really appreciate, Congratulations, but, could you please tell us how can we use these function classes in a basic model inside OnInit(), OnDeinit(), OnTick() and OnTimer()? How should i call your BPB NeuralNet Functions the right manner inside an EA code?! Let's suppose a 'XOR Problem', or even a basic structure with last ten ticks value being placed on input, so i can understand how it works, how to define quantity of hidden layers, where do i insert input's and recall the output?
I've been integrating via Python socket one BPB external code, but i think is reasonable to believe your code could be very effective directly inside my EA.
Thanks in advance!
True. I would really appreciate it.
This is a very inspiring article. I dedicated two days to read and analyse the codes thereign, and trust me, he took his time.
However, I tried implementing the functions in an EA but keep getting stuck. I'm not sure how the functions flow. A sample input and training session would really help.
Actually, today I came back hoping to find more articles on the same. I have faith in the author. I will wait for more articles.
It is not a must that you publish a sample. Just briefly explain how input arrays are fed into the system, and how the results can be viewed. I have studied the code daily in a week, I understand the codes to some extent, but still cannot figure out how the methods are interconnected. It's an urgent matter, but I guess I can't rush you. If the answer comes after I've figured it out, I'd still be thankful.
Please respond whenever you can.
It is not a must that you publish a sample. Just briefly explain how input arrays are fed into the system, and how the results can be viewed. I have studied the code daily in a week, I understand the codes to some extent, but still cannot figure out how the methods are interconnected. It's an urgent matter, but I guess I can't rush you. If the answer comes after I've figured it out, I'd still be thankful.
Please respond whenever you can.
I figured it out. All the work has been simplified down to CNet class.
Thank you for this awesome article.
Peace.
I figured it out. All the work has been simplified down to CNet class.
Thank you for this awesome article.
Peace.