> is there a resource other than source code I'm missing ? First, you should understand the math you need to be able to implement the algorithms, then you shou

> is there a resource other than source code I'm missing ? First, you should understand the math you need to be able to implement the algorithms, then you should learn the algorithms. You may "get" the code, but you will never understand it if you don't understand the mathematical objects it represents. Simple example, linear regression, we can compute the solution without iterative improvements as in Neural Networks, why? How do we actually code that? What is the pseudo-inverse that we are computing doing there? From here, what's the relationship of that with Newton's method for numerical optimisation? Why does alternating least squares even work? The code will _never_ explain the underlying mathematics, it can only represent them. I am not really sure what you are expecting for such a vast field. I recommend reading Elements of statistical learning or Bishop's book or Murphy's Probabilistic ML.