[nicolewhite]

I enjoyed his comments on Tensorflow.

> It’s really bad to use. There’s so much hype around it, but the number of people who are actually using it to build real things that make a difference is probably very low.

I wonder how many data scientists out there are actually developing Tensorflow models for a mission-critical project at work. I'm not. I have used Tensorflow successfully within my personal projects, but I've yet to need it for anything "real."

[mslate]

We used it for a sales email classification problem--it significantly out-performed our conventional approaches (i.e. logistic regression + bag-of-words), but we were not PhDs and none of our job titles were "data scientist" so I guess that makes us charlatans ;)

That service offering among the rest of the business was marginal so it never became an offering that our sales team pitched our customers very aggressively, so in this particular case TensorFlow did not push the needle so-to-speak.

[amrrs]

Wondering what Tensorflow has to do with that out-performance since it must be all about the model/algorithm that you implemented in that - like you could've had a TF code running the same conventional approach you mentioned above - which wouldn't have done any magic. Isn't it the algorithm like a convnet doing the magic rather than TF itself responsible for it?

[mslate]

Yes, TF is merely a framework implementing convolutional neural nets, not a novel implementation of them.

We chose TF over other convolutional neural net libraries because it was 1. Python and 2. heavily sponsored by Google.

[brucephillips]

What TF model did you use?

[mslate]

This was "ages" ago, pre 1.0 so ~2 years ago. TBH, I can't recall which model we used. We ran it in production for several months on a proprietary training dataset of 30k emails, re-training it once a week.

I regret not following through more on that project, but hey, you've only got so much political capital to burn when people ask you "and how does it make us money?"

[hyperbovine]

I'm currently using TF for a scientific algorithm that's completely unrelated to deep learning. The speedup over our previous solution is probably on the order of 1000x. There's nothing magical about Tensorflow, we were just too lazy/busy to dive deep on the legacy code, GPUify it, etc. Tensorflow let me do that in a couple of days. So, that's a win. OTOH I completely agree that the API and docs are completely inscrutable at times. Presumably Google is happy with it.

[cosmic_ape]

As other comments mention, if Tensorflow is seen for what it is - a framework for computation, rather than just "a deep learning thingy", it may be pretty useful.

It is probably quite far from a standard usage, but Tensorflow may be used to write some custom graphical models inference, for example. To be practical these algorithms can not be implemented in, say, pure python.

The point is Tensorflow gets you pretty close to assembly level computation. The alternative is to write in, say, cython - which is much more time consuming to write, and does not give you parallelization for free. Another alternative I guess would be torch, but that is the same as tensorflow the way I see it.