The list is for beginners.Ofcourse data science is a huge domain and you can't actually make a roadmap to be a master in this field but I am sure this list will help people to get basic understanding of what to read and how to workout things.
I think the list is more helpful for someone a step or two beyond beginner. A true beginner is going to look at that and be scared. Once they've read a few introductory things, they'll be able to go back and make better sense of it, for sure. (I showed this to a friend of mine who's interested in learning data science and that was his reaction, so I am generalising, but I think it's a fair generalisation.)
Yes its true that neural nets are not used in commercial systems because its computation intensive and also needs huge infrastructure even for things like finding cat images out of youtube videos but the results are far more accurate then any of the statistical methods used in commercial systems. There is time-performance trade-off when it comes to choosing statistical methods over neural nets and also it's a field yet to be explored. I will be writing a series of article on neural nets for beginners and advanced programmers . Please stay tuned.
Why do you think that they are not used in commercial systems? As far as I remember from leckeres of Geoffrey Hinton they are now used quite extensively in speech recognition systems on smartphones, etc.
they are used in speech recognition systems "for smartphones", not "on smartphones." Quite a big difference. None of these systems will work if your phone is not connected to a server that does the decoding work.
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