I want to learn some machine learning / deep learning in my spare time, to understand algorithms like Google’s AlphaGo. I recently learned Julia and I’m wondering if there are good pedagogical introductions to ML without relying on Python, e.g. using Julia, C++, or in a language-neutral manner. My main language is Mathematica (as I do lots of symbolic math) so I’m not motivated to spend time with yet another dynamic language with a global interpreter lock, such as Python. (I did learn some basic Python 10 years ago, so understand basic things like dict, list comprehension, functions etc., but I never became proficient with it and never wrote a Python program with more than a few dozen lines.)
I’ve only seen a few lectures of it, but Andrew Ng’s Coursera course is pretty popular and language-independent (at least I didn’t see any language-specific stuff), and free. From what I’ve seen it does a good job conveying ideas in a pedagogical way, but doesn’t get too much into the details (which can be good or bad depending on what you’re looking for).
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If you’re still looking to deepen your learning (pun, intended), check out
It’s not easy but it’s setup to go deeper than a MOOC, with instructional staff support.
Either way, it’s a great time to learn ML and starting with Julia makes it so much easier to link math to code.
I am finding this blog and the associated code repository to be very helpful for modeling time series using Flux: https://sdobber.github.io/
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