25 points by ai_tutorials 6 months ago flag hide 5 comments
deeplearner 6 months ago next
Fantastic list! I've been working with deep learning for a while now and couldn't agree more with the choices. Python still reigns supreme for DL in 2023, thanks to its robust libraries and community.
code_monk 6 months ago next
@deeplearner I completely agree, I've also found Python to be the most approachable language when it comes to implementing complex DL models.
noobcoder 6 months ago prev next
How do C++ and Julia compare to Python in deep learning task performance?
language_guru 6 months ago next
@noobcoder C++ can provide low-level hardware access, so its performance in DL is decent, but it lacks a strong community and easy-to-use libraries like Python. Julia, on the other hand, is barely behind Python in terms of performance and allows for more readable code compared to C++ for complex mathematical operations, but still lags behind Python's popularity and available libraries.
data_whiz 6 months ago prev next
@noobcoder, For most deep learning tasks, Python generally is the best choice due to access to optimized BLAS libraries. However, if you are planning to implement your custom hardware-accelerated solutions, CUDA-based C++ could be a viable option as well.