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Differentiable programming for gradient-based machine learning

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Re: Differentiable programming for gradient-based machine learning

#11

Post by dunefox »

dash2 wrote: post #2

Has anyone got experience in using other things than python for machine learning? What are the rivals out there?

I'm using Julia instead of Python for NLP and it's great. If I need a python library I just use PyCall and so far it has worked flawlessly.

Re: Differentiable programming for gradient-based machine learning

#12

Post by dunefox »

gumby wrote: post #4
dash2 wrote: post #2

Has anyone got experience in using other things than python for machine learning? What are the rivals out there?

In my experience: Julia, Java, C++. Lack of libraries in a pain; OTOH the quality of most libraries (in general) tends to be poor. Examples of library issues: there is C++ support in tensor flow, but not for training.

Julia doesn't lack libraries and the quality imo is pretty good. Did you look at PyCall.jl?

Re: Differentiable programming for gradient-based machine learning

#13

Post by dash2 »

dunefox wrote: post #11
dash2 wrote: post #2

Has anyone got experience in using other things than python for machine learning? What are the rivals out there?

I'm using Julia instead of Python for NLP and it's great. If I need a python library I just use PyCall and so far it has worked flawlessly.

Can you say a bit about the advantages of each? I'm just starting to learn deep learning and planning to use it for NLP. I'm taking the python-based fast.ai course. But I'm tempted by the elegance of Julia.

Re: Differentiable programming for gradient-based machine learning

#14

Post by dunefox »

dash2 wrote: post #13
dunefox wrote: post #11

Earlier quoted context omitted.

I'm using Julia instead of Python for NLP and it's great. If I need a python library I just use PyCall and so far it has worked flawlessly.

Can you say a bit about the advantages of each? I'm just starting to learn deep learning and planning to use it for NLP. I'm taking the python-based fast.ai course. But I'm tempted by the elegance of Julia.

If you're starting you should use Python and when you have a more solid grasp you can switch to Julia. That's mostly due to the fact that there are much more tutorials and resources for dl in python.

Re: Differentiable programming for gradient-based machine learning

#16

Post by undefinednull »

dunefox wrote: post #12
gumby wrote: post #4

Earlier quoted context omitted.

In my experience: Julia, Java, C++. Lack of libraries in a pain; OTOH the quality of most libraries (in general) tends to be poor. Examples of library issues: there is C++ support in tensor flow, but not for training.

Julia doesn't lack libraries and the quality imo is pretty good. Did you look at PyCall.jl?

Interesting! I did not yet! will take a look. I remember swift was working on something similar to seamlessly call Python.

Re: Differentiable programming for gradient-based machine learning

#17

Post by samuell »

dash2 wrote: post #2

Has anyone got experience in using other things than python for machine learning? What are the rivals out there?

There is a rather active community for Go in data science and machine learning. +1700 members on the #data-science channel on the Gopher slack, a few books on the topic, and a few libraries:

Books

Machine learning with Go - https://www.amazon.com/Machine-Learning-Classification-Time-...

Go Machine Learning Projects - https://www.amazon.com/Go-Machine-Learning-Projects-Ultimate...

Libraries

https://gorgonia.org/

https://www.gonum.org/

A good talk (although from a from years back), explaining why Go for ML could be a good idea:

https://www.youtube.com/watch?v=D5tDubyXLrQ

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