So I've been trying to understand neural networks ever since I came across Adam Geitgey's blog on machine learning. I've read as much as I can on the subject (that I can grasp) and believe I understand all the broad concepts and some of the workings (despite being very weak in maths), neurons, synapses, weights, cost functions, backpropagation etc. However, I've not been able to figure out how to translate real world problems into a neural network solution.
Case in point, Adam Geitgey gives as an example usage, a house price prediction system where given a data set containing No. of bedrooms, Sq. feet, Neighborhood and Sale price you can train a neural network to be able to predict the price of a house. However he stops short of actually implementing a possible solution in code. The closest he gets, by way of an example, is basic a function demonstrating how you'd implement weights:
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood): price = 0 # a little pinch of this price += num_of_bedrooms * 1.0 # and a big pinch of that price += sqft * 1.0 # maybe a handful of this price += neighborhood * 1.0 # and finally, just a little extra salt for good measure price += 1.0 return price
Other resources seem to focus more heavily on the maths and the only basic code example I could find that I understand (i.e. that isn't some all singing, all dancing image classification codebase) is an implementation that trains a neural network to be an XOR gate that deals only in 1's and 0's.
So there's a gap in my knowledge that I just can't seem to bridge. If we return to the house price prediction problem, hows does one make the data suitable for feeding into a neural network? For example:
- No. of bedrooms: 3
- Sq. feet: 2000
- Neighborhood: Normaltown
- Sale price: $250,000
Can you just feed 3 and 2000 directly into the neural network because they are numbers? Or do you need to transform them into something else? Similarly what about the Normaltown value, that's a string, how do you go about translating it into a value a neural network can understand? Can you just pick a number, like an index, so long as it's consistent throughout the data?
Most of the neural network examples I've seen the numbers passing between layers are either 0 to 1 or -1 to 1. So at the end of processing, how do you transform the output value to something usable like $185,000?
I know the house price prediction example probably isn't a particularly useful problem given that it's been massively oversimplified to just three data points. But I just feel that if I could get over this hurdle and write an extremely basic app that trains using pseudo real-life data and spits out a pseudo real-life answer than I'll have broken the back of it and be able to kick on and delve further into machine learning.