Originally posted here Sometimes it can save a lot of time to stop and think if ML/DL is indeed the right approach to solve your problem rather than realising that after throwing a ton of data at a network without getting anywhere and potentially being outperformed by a simple classical algorithm. Here’s where ML shines: When the inputs are noisy When the solution cannot be described in human defined rules When the problem cannot be entirely defined and your solution needs to scale for dive...
