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MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors

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by , 05-17-2024 at 07:06 PM (174 Views)
      
   
We continue the series on MQL5 wizard implementation by looking into Neural Architecture Search while specifically dwelling on the role Eigen Vectors can play in making this process, of expediting network training, more efficient. Neural networks are arguably the fitting of a curve to a set of data in that they help come up with a formulaic expression that, when applied to input data (x), provides a target value (y) just like a quadratic equation does with a curve. The x and y data points though can be, and in fact are often, multidimensional, which is why neural networks have gained a lot of popularity. Nonetheless, the principle of coming up with a formulaic expression does remain, which is why neural networks are simply a means of arriving at this but not the only way of doing so.
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