Machine Learning Crash Course: Part 2
Now that there's a bunch of AI/ML-related links in the front page, probably now is the best time to ask:As I learn deep learning, from the practical point-of-view, I found that the idea is simply to feed some "black box" with labeled data so next time it can give you correct label given unlabeled data. In essence, it's pattern recognition. What do you think?And then, as I try to find use cases for ML (you know, finding problem for the solution), I found that actually, many problems that can be solved with ML can actually be solved with rules. For example, detecting transaction fraud. You just need to find the right rules/formula. Forget ML, if you can't hardcode if-else, just use rules engine. What do you think?So, I'm starting to think that ML is good for solving problems where (1) we're too lazy to formulate the rules, or (2) the data is too complex/big to analyze by rules (as in, understanding image or voice). What do you think?