I learn about neural networks with a heavy excercise. I have products and want to forecast the revenue. I have 10 features. But 4 features have large numbers of expressions. So my "group" features has over 500 different groups (of products). If I do binarization with the features I end up with vectors of over 1000 dimensions as input. All the product classes are names with letters and numbers such as "100XA9". Now there are 500 of those. For a neural network I have to do binarization with those names. Is that right? Then my input layer is 1000, right?
My question is this "normal"? And is this problematic for computation?
I mean this is a typical question. A store with many products (like over 500) will forecast its revenue based on the prices and other features.
I hope you can help me to untestand how to use neural networks for classification.