# how to calculate quadratic features in computer vision Neural Network

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?

• it's common to use powers of two for sizing filters or dense layers in neural networks – Vadim Smolyakov Aug 15 '17 at 18:26

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