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Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

sebastianraschka.com

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Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#81

Post by srean »

nxb wrote: post #77
leni536 wrote: post #76

Earlier quoted context omitted.

>Now, all that matters is which library makes it easiest to get custom code onto the GPU. Python and Lua seem to be winning there, by far. This is interesting. How is it possible that python and lua have more efficient wrappers around GPU libraries? Also there are many GPU libraries for C/C++ too. Armadillo can use NVBLAS as a backend too. I'm not sure if I get your point of C/C++ being slow.

It's not about wrapping. The real power is in the cross-compilation of expressions and entire complex data pipelines, from a simple-as-possible high-level language into GPU language. That's the power at the core of, e.g. Theano.

Compiling high level instructions to different hardware backends is hardly an exclusive feature of python and lua libraries. Google would swamp you with hits if you were to search

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#82

Post by daniel-levin »

sampo wrote: post #9

Andrew Ng said in the Coursera Machine learning class that according to his experience, students implement the course homework faster in Octave/Matlab than in Python. But yes, the point of that course is to implement and play around with small numerical algorithms, whereas the linked blog is about someone who mainly calls existing machine learning libraries from Python. Ref. https://news.ycombinator.com/item?id=44858…

This could also be because students are already familiar with doing numerical computations in MATLAB from courses they'd done before. It is conceivable that students that take a machine learning course have already done some numerical analysis or scientific programming, likely using MATLAB.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#83

Post by nxb »

srean wrote: post #81
nxb wrote: post #77

Earlier quoted context omitted.

It's not about wrapping. The real power is in the cross-compilation of expressions and entire complex data pipelines, from a simple-as-possible high-level language into GPU language. That's the power at the core of, e.g. Theano.

Compiling high level instructions to different hardware backends is hardly an exclusive feature of python and lua libraries. Google would swamp you with hits if you were to search

Show me one C/C++ library that competes with Theano or Torch7?

Google / Facebook and many other huge companies are using Theano and Torch7 in production, at scale. The ML industry has been continuously moving in this direction for years now.

On these optimized ML systems, only a tiny fraction of CPU time is spent outside of the GPU. The goal in many of these companies is to migrate all tasks that can be done on GPUs to GPUs, as soon as possible. It's far faster and more cost efficient.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#84

Post by plafl »

leni536 wrote: post #42

And there is nothing wrong with C++. For linear algebra I use the armadillo library and it's really a nice wrapper around LAPACK and BLAS (and fast!). For some reason scientists are somewhat afraid of C++. For some reason you "have to" prototype in an "easier" language. Sure, you can't use C++ as a calculator as opposed to interpreted languages, but I see people being stuck with their computations at the prototyping…

Writing scientific code is hard both in C++ and python, but reading and manipulating data and visualizing it is way easier in python. It's also easier to install and use third party libraries.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#85

Post by novocaine »

nxb wrote: post #83
srean wrote: post #81

Earlier quoted context omitted.

Compiling high level instructions to different hardware backends is hardly an exclusive feature of python and lua libraries. Google would swamp you with hits if you were to search

Show me one C/C++ library that competes with Theano or Torch7? Google / Facebook and many other huge companies are using Theano and Torch7 in production, at scale. The ML industry has been continuously moving in this direction for years now. On these optimized ML systems, only a tiny fraction of CPU time is spent outside of the GPU. The goal in many of these companies is to migrate all tasks that can be done on GPUs…

Do you have some sources demonstrating that google and facebook are using them in scaled production? My impression was that presently these were more for research and prototyping.

I would have thought that if you were going to run prod systems in the gpu you would actually write CUDA (C++) or similar to avoid the inefficiency of the abstraction layer.

(also, this comment is bordering on the uncivil).

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#86

Post by srean »

nxb wrote: post #83
srean wrote: post #81

Earlier quoted context omitted.

Compiling high level instructions to different hardware backends is hardly an exclusive feature of python and lua libraries. Google would swamp you with hits if you were to search

Show me one C/C++ library that competes with Theano or Torch7? Google / Facebook and many other huge companies are using Theano and Torch7 in production, at scale. The ML industry has been continuously moving in this direction for years now. On these optimized ML systems, only a tiny fraction of CPU time is spent outside of the GPU. The goal in many of these companies is to migrate all tasks that can be done on GPUs…

> show me ...

I can only bring the horse to the water (or Google as its sometimes called these days) :)

>Google/Facebook and many other huge companies ... You are totally wrong.

That totally settles it then thank you, who am I to argue and surely there are no ML jobs that spend time outside of GPU.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#87

Post by tdees40 »

leni536 wrote: post #42

And there is nothing wrong with C++. For linear algebra I use the armadillo library and it's really a nice wrapper around LAPACK and BLAS (and fast!). For some reason scientists are somewhat afraid of C++. For some reason you "have to" prototype in an "easier" language. Sure, you can't use C++ as a calculator as opposed to interpreted languages, but I see people being stuck with their computations at the prototyping…

It's unimaginable for me to do most of this stuff without a REPL. That's the show-stopper for me with C++.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#88

Post by geebee »

rm999 wrote: post #7

I switched from mostly using R to Python about a year ago for gluing together my data pipeline (from data source all the way to production models and frontends/visualizations). It hasn't really impacted what I'm capable of doing or my productivity, except the standard extra googling that comes in the first couple years I use any language. The main reason I went for Python is purely practical: it's a language people o…

"for gluing together my data pipeline"

Yep. This is exactly why I gravitated toward python and scikit-learn. So much of data science is just getting the data into the right format. Grab it from this file, and this database, and this web service, then get it formatted into this table structure, and then clean it with this filter, and then plug these holes this way and those other holes that way, and now you're finally ready for a random forest baseline.

Python is a really good language for merging and parsing data from lots of different sources. For many problems, a general purpose language with very good data science library may actually be the better choice than a dedicated data science language/environment.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#89

Post by dswalter »

geomark wrote: post #26

I just completed the Coursera data science track which took me from a complete R newbie to being at least somewhat proficient. Having previously used Python for a quite a bit of web programming, I disliked R at first except for its power in statistical programming. But I've since discovered a number of great R packages that make it a pleasure to use for things I would normally turn to Python for. Like I recently disc…

You bring up a good point in favor of R: Hadley Wickham and the rest of the RStudio people.

Packages like {ggplot2,rvest,dplyr,devtools, etc.} are basically creating a sub-language for R.

I use both at the moment, but I echo the OP's ideas that R's target audience is statisticians, where Python's target audience is broader and includes statisticians and computer scientists. And Python's syntax is nicer to work with. That's why it's become the primary glue language.

That said, the overhead for learning Python as your first data science language is a bit problematic for me, as you basically have to learn Python followed by Python's data science tools (pandas, matplotlib, etc). whereas with R, you're learning the language and the data science tools at the same time, even if they're a bit idiosyncratic.

Re: Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

#90

Post by digitalzombie »

rasbt wrote: post #46
Adam_O wrote: post #17

From the perspective of a student, most of the good online analytics/data analysis/stats courses use R, so it is hard to get away from it while learning the material. Once you get the base concepts down, switching to python shouldn't be hard. I think most people still prefer ggplot2 for visualization though. Whenever I use R I feel like a statistician, I can feel that 'cold rigor' emanating from the language. But in…

Yes, I think you are right. Out of curiosity, when I browsed over Coursera's course catalog, most data science related material seems to be taught in Matlab or R (however, there are also others, e.g., Klein's Linear Algebra class in Python). Personally, I think that instructors shouldn't enforce a language requirement. I believe for big platforms such as coursera it shouldn't be to hard to run an respective interpret…

Most classes have to teach the subject and how to program. Programming is beginning to be an essential skills so they have to choose a language to teach.

Also those classes that chose R, from my experiences, are non CS classes, the professor are from other discipline. They just want a tool that solve their need quick. An example is the Princeton's Stat class, the professor is a humanity major. The class gave us tons of data and we had to do ANOVA and such and we needed a computer to crunch so number can't do by hand. So he chose R which he uses a lot.

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