DeepMind AI reading list [pdf]
This doesn’t make much sense. If you are not already trained in vector calculus, functional analysis and basic classifier and regression algorithms, then most of these reading list items are completely inapplicable (or even dangerous, like when someone reads some blog posts about slapping together neural nets in Keras and suddenly thinks they can build a model suitable for production).<p>On the other hand if you are trained in ML, most of these are not detailed or extended enough to give you anything useful. Doing “a hacker’s intro to X” over and over really, really doesn’t give you any skills. This is particularly true for deep domains like reinforcement learning, computer vision / image processing, and natural language processing.<p>Meanwhile, basic design of experiments for A/B testing, explanatory modeling and simple regressions is a fraught area. Not understanding extremely rigorous details about hypothesis testing, model checking, limitations of statistical significance, etc., can lead to wildly incorrect inferences from poor models that non-experts will completely fail to detect.<p>At best this list seems like “special topics to seem trendy in AI without getting deep / practical insight into any particular area.” There are one or two minor exceptions in the list.