[rylz]

Maybe you're right, but I think there's something to be said for teaching applied ML separate from the theory. I took a very in-depth theory-focused ML class at Caltech, in which lectures made up a thoroughly rigorous mathematical introduction, and the problem sets were only maybe 10% applications. I and my classmates came out of the class feeling super comfortable with the theory behind learning in general and how it applied to many of the standard ML algorithms, but without any experience actually working with very large data sets and building ML algorithm implementations that scale. That's what I'm hoping this book will help with, and I think it is appropriate to separate that kind of material from the theory.

[iqster]

Good point. Does one really need to know how to code up a neural network in order to just use it? This has been my biggest frustration with some teachers of Machine Learning. They need to abstract away the innards and provide a usable high-level interface. I know this is hard to do. ML isn't magic ... you have to know what you are doing. But the same can be said for the automobile when it was first invented. Today, to effectively use a car, you don't need to know anything about engines.