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?
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