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Machine Learning for Developers

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Re: Machine Learning for Developers

#41

Post by p1esk »

mathgeek wrote: post #23
mindcrime wrote: post #17

Earlier quoted context omitted.

Java and Scala? Who uses that in ML? Python has long been the best language for ML You're kidding, right? Java has been extremely popular for ML for a long time. Not to cast any shade on Python, but I'd say Java and Python are roughly equivalent in this regard. Both have good libraries for various ML tasks and both are very popular in the domain. For reference, a quick search on mloss.org finds 84 projects identified…

I feel that much like programming in the corporate world, Python is often used to teach ML while Java is more often used to implement it.

Two out of three most popular deep learning libraries have python front end (theano and caffe). The third one (torch) uses Lua.

Re: Machine Learning for Developers

#42

Post by hugh4 »

wyldfire wrote: post #34
nostrademons wrote: post #26

Earlier quoted context omitted.

Is that any different from a bunch of developers plugging in magic numbers into a formula that they made up, which (to a first approximation) is roughly what happens now? Realistically, the outcome will be the same as it is now: those firms whose models don't reflect reality will blow up, those whose do will get bigger, a few will get too big to fail off some very confidently-expressed models and make a lot of people…

> those firms whose models don't reflect reality will blow up, I'm picturing one of those dystopic films/novels where the main character is deleted/fired/jailed as a result of an algorithm error. Yes, in real life the trends will overcome the bad models. But just think of the potential consequences for harm on an individual basis!

Isn't that just the plot of Minority Report?

Think of the possibilities of machine learning for detecting precrime!

Re: Machine Learning for Developers

#43

Post by vosper »

RyanZAG wrote: post #13
p1esk wrote: post #3

Java and Scala? Who uses that in ML? Python has long been the best language for ML, with some competition from Matlab.

Python isn't used for much ML in the field from my experience. It is heavily used for teaching and learning about ML - but for actual production ML, I've seen mostly compiled languages. The main reason is that ML is highly parallelizable and Python isn't terribly good at that. Plus you need to crunch large datasets and speed becomes important. So, respectfully, lots of people use languages other than Python for ML, a…

I work with a team that is doing large scale ML and CV on millions of pages and videos. It's a production system tied to millions of dollars in revenue. Everything's done in Python.

Re: Machine Learning for Developers

#44

Post by mystique »

zxcvvcxz wrote: post #25

Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.

A decent CS undergrad degree decade ago included abstract math concepts. I took Engineering math, Information Theory, Numerical analysis, Probability, Simulation in my sophomore and Junior years. NLP and AI were electives in Senior year.

As a Junior, we were building toy programs that do Operations research type of work - solving linear equations via various matrix operations, design optimal queue processes based on poisson process.

Assuming a software engineer is a CS undergrad, he/she most likely has good footing to learn more by themselves.

Re: Machine Learning for Developers

#46

Post by jorgemf »

aficionado wrote: post #38
zxcvvcxz wrote: post #25

Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.

I'm a little bit worried. At least at the same level as when I see a bunch of developers compiling programs without much understanding of what an LL(k) parser does, or how a pushdown automaton works, or what a Turing machine is. I usually feel the same every time I see an elevator without a liftman, don't compute a square root by hand, or hear about Google self-driving cars.

The difference is that the software or the elevator will work but the statistical model is wrong and doesn't work. It is like the elevator only lift people above 120 and below 90 and for the others it just don't work or take you to the wrong floor.

Re: Machine Learning for Developers

#47

Post by chriswarbo »

jorgemf wrote: post #46
aficionado wrote: post #38

Earlier quoted context omitted.

I'm a little bit worried. At least at the same level as when I see a bunch of developers compiling programs without much understanding of what an LL(k) parser does, or how a pushdown automaton works, or what a Turing machine is. I usually feel the same every time I see an elevator without a liftman, don't compute a square root by hand, or hear about Google self-driving cars.

The difference is that the software or the elevator will work but the statistical model is wrong and doesn't work. It is like the elevator only lift people above 120 and below 90 and for the others it just don't work or take you to the wrong floor.

> The difference is that the software... will work

Lots of software doesn't work. Is there a substantial difference between putting an overfitting model in production, and putting a poorly tested program in production?

Re: Machine Learning for Developers

#48

Post by argonaut »

mystique wrote: post #44
zxcvvcxz wrote: post #25

Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.

A decent CS undergrad degree decade ago included abstract math concepts. I took Engineering math, Information Theory, Numerical analysis, Probability, Simulation in my sophomore and Junior years. NLP and AI were electives in Senior year. As a Junior, we were building toy programs that do Operations research type of work - solving linear equations via various matrix operations, design optimal queue processes based on…

In all fairness, that's pretty atypical of a standard CS degree. In my anecdotal experience (knowing people that went to Stanford/Berkeley/MIT/CMU), most people take at most 1 probability class, 1 linear algebra class, and maybe 1 AI/ML class. Info theory, NLP, numerical analysis, optimization, etc. are not at all common.

Re: Machine Learning for Developers

#49

Post by argonaut »

aficionado wrote: post #38
zxcvvcxz wrote: post #25

Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.

I'm a little bit worried. At least at the same level as when I see a bunch of developers compiling programs without much understanding of what an LL(k) parser does, or how a pushdown automaton works, or what a Turing machine is. I usually feel the same every time I see an elevator without a liftman, don't compute a square root by hand, or hear about Google self-driving cars.

It's different. In ML the model, the analysis, and the insights, are the product. In general software engineering, your compiler is not your product.
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