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I am recently watching some tutorials for deep learning from Dr Andrew Ng on Youtube. Link is hereThe Youtube video

There is a concept of number of features in convolutional neural network in TensorFlow's tutorial.

I don't quite understand why the feature is 32 or 64 here in conv layer1 or layer2?

Then I came to the video, there is also the concept of Quadratic features. It is calculated as 3 million however. But how is it calculated?

Are the two features related in concept?

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  • $\begingroup$ it's common to use powers of two for sizing filters or dense layers in neural networks $\endgroup$ – Vadim Smolyakov Aug 15 '17 at 18:26
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Sorry for being so late in the response. I have just read your message.

The instructor is using $\frac{x^2}2$. So $\frac{2500\times 2500}2$, and this will get approx $3$ millions features.

BR

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What this means is:

1) 50x50 pixels image means 2500 unit pixels in your image. Then each unit is a feature, i.e.,

x1=pixel 1, x2 = pixel 2, .......x2500 = pixel 2500. A quadratic function means that you take all possible combinations of xi.xj, for example x1^2, x1.x2, x1.x3,....,x1.x2500, etc... this will give you a better outcome than considering just a linear function..., just get all the possible combinations and count them and you will get around n^2/2 = 2500x2500/2 = 3M

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