Ask HN: Whats the best way to learn C++ for Deep learning?

I agree with this, but I would offer that #3 is more an integration of #1 and #2 rather than a "learn", as your last sentence points out with "once you have done 1) and 2)".

So I believe you should more strongly recommend 1) and 2) and include what a later poster added about various frameworks: "BLAS, LAPACK, and maybe the whole CUDA family (CUDA, cuBLAS, cuDNN, etc)", see @madenine.

I'd agree with the shout out for Tour of C++ as a good first book. Unlike most C++ intros, it's concise (192 pages) and up-to-date enough to exemplify C++'s standard forms (esp. 98 and 03). If you plan to develop in C++ you'll want other books with more examples and reference, but these will shift the focus more onto trees than forest (at ~1000 ppg).

Accelerated C++ by Koenig and Moo is another nice short overview of C++, but assumes more familiarity with C and OOP principles.

Thanks! Mostly it was out of a desire to learn more about the actual algorithms behind AI, and trying to figure out if it's a field I might enjoy working in. I really enjoy building things from the ground up, and understanding how each part works.

I guess for me, having been using c++ as my preferred language for 25 years I just now read api's. When new C++ features are added I get to know then by practicing using them in small programs then into production code.

Stroustrup and Meyers are the two most well known names. I buy all their books and I read them and practice all exercises and problems if the book has them.

What is your reason for learning "C++ for deep learning"?

This will kind of define how to go about doing it.

I can think of a few different reasons you might want to do this:

* You DL code in another framework is slow, and you have some custom C++ you want to write to speed it up. For this, you probably want to learn both high performance C++ and/or CUDA kernel development. You can probably avoid diving completely into C++, and just call a couple of optimized routines from python (and whatever other deep learning framework you are using in python). In this case, it might be worth looking into Tensor Comprehensions.

* You are taking someone elses DL algorithm and moving it to C++. In this case, you still don't need to be diving too much into C++, Ideally, you probably want to use something like ONNX and Caffe, and then call into them with some python wrapper. If you want the whole thing to be in C++, then you are still going to use something like ONNX and Caffe, but you will be writing mostly application code to support it.

* You want to help with the development of Pytorch, Tensorflow, Caffe or anther deep learning framework. In this case, you need to know CUDA kernel development, performant C++ idioms, and good C++ application development idioms.

These are pretty different flavors of C++, which makes it hard to give you a good answer.