[solaxun]

Books in the little series are all about "learning by doing", and teach using the Socratic method (question/answer). Each concept is introduced with small problems that build upon each other. You are forced to get your hands dirty from the beginning, passive learning is not really an option. For most people, I think this helps facilitate a much deeper understanding than just reading a text.

I always had trouble with recursive functions when I was new to programming, and many recommended working through "the little schemer" to solve that problem. It was a tough read for me, but the investment was well worth it and it did for me what it said on the tin. I didn't have nearly as much trouble with recursion after that book, but an unfortunate side effect was developing an affinity for lisps which I haven't yet shaken.

[m463]

> "learning by doing"

Too little of that in this world nowadays. Too much ios and not enough arch linux.

[boredemployee]

ok cool! thanks for explaining. do you think a beginner in the world of AI and programming would benefit from this DL book as well?

[solaxun]

Tough to answer since I've only read the two intro chapters currently available. I'm still in the early stages myself with with Deep Learning but have years of experience with programming and lisp, so I'm coming in with a background that might bias my views.

If you have never used lisp, you need to be patient with the notation and resist the urge to balk at what is unfamiliar. I remember grumbling quite a bit when I first went through TLS, but that phase was over pretty quickly and within days I had no trouble following the code.

Are there better ways to introduce deep learning and neural networks? Maybe.. but I like "little" books and there is no better way to learn something than by building it yourself. For that reason alone I'd recommend the book sight unseen (but with the knowledge of prior little books). I do think choosing what is likely an unfamiliar language for most may be somewhat of an impediment for an already challenging topic, but scheme is a simple language and allows the author to focus on the ML concepts.

I'm highly confident you'll learn a lot, if you put in the work. For DL as a separate topic, the best resources I've found are:

  - 3 blue 1 brown Neural Net playlist
  - Karpathy's "The spelled-out intro to neural networks and backpropagation"