
A New Batch of Deep Learning Startups to Watch for in 2017 - scrolib
http://www.scrolib.com/2017/01/batch-deep-learning-startups-watch-2017/
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karambahh
Sorry to be negative, but can we stop touting companies solely on the
mathematical concepts they rely on?

Deep learning techniques are indeed very efficient at solving a very large
spectrum of problems, but it does not mean that every company using DL is
remarkable. That's remarkable is the goal they achieve or aim to achieve.

If they use decades old technologies in a meaningful way for their target
industry, does it make their company less likely to suceed? No it does not,
for all that matters is solving the customers' issues...

My company use DL in several fields, but what we sell is simply a solution to
customers needs...

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deepnotderp
Agreed, silly deepmind! /s

The fact of the matter is, if you are smart enough, you can get acquihired for
a lot of money. DeepMind, Geometric Intelligence, Deep Blue Labs, Vision
Factory, TupleJump, Turi, etc. are all examples.

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karambahh
DeepMind delivered value on several fields.

They happen to rely (partly) on DL to deliver this value, but they got acqui-
hired not because "we are doing deep learning" but because "we have technology
that can lower your costs/increase your profits"...

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iandanforth
They did? My impression was that until the server farm optimization they were
a cost center for Google.

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karambahh
The server farm optimization is massive indeed.

They also provided value before but nothing has been made public about it yet,
unfortunately.

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zxcvvcxz
I'd just like to interject for moment. What you're refering to as Deep
Learning, is in fact, non-convex optimization, or as I've recently taken to
calling it, generalized function approximation. Deep Learning is not a
technology unto itself, but rather a specific type of mathematical
optimization that has been known about for decades, is based on stochastic
gradient descent, and only now is being featured more prominently due to
enhaced processing power.

Many computer users run some form of mathematical optimization everyday,
without realizing it. Through a peculiar turn of events, the optimizations
which are widely used today are often called Machine Learning, and many of its
users are not aware that it is basically non-convex optimization, or high
dimensional function approximation.

There really is Deep Learning, and these people are using it, but it is just a
part of the optimization mathematics they use. Deep Learning is the kernel:
the program in the system that allocates the machine's resources to the other
programs that you run. The kernel is an essential part of an operating system,
but useless by itself; it can only function in the context of a complete
rigorous mathematical framework. Deep Learning is normally used in combination
with other ideas in classical mathematical optimization: the whole system is
basically stochastic gradient descent with GPUs added, or large-scale
numerical optimization. All the so-called Deep Learning algorithms are really
algorithms of generalized function approximation!

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brandonb
This seems like a sincere comment, so in case it's helpful, here's my
interpretation of why others may be downvoting it.

Deep learning practitioners are aware that they're using non-convex
optimization, stochastic gradient descent, and so on. In fact, those topics
are core to modern deep learning research and explicitly acknowledged in most
published research: LSTMs were invented to solve the vanishing gradient
problem, Restricted Boltzmann Machines were used as a pre-training step to
avoid local minima, and optimizers like ADAM have explicit guarantees about
things like convergence.

You may know all this stuff already--not sure, based on your comment above.

If not, here are a few example papers from mainstream AI researchers
explicitly talking about deep learning as an optimization problem or function
approximator: [1] Why Does Unsupervised Pre-training Help Deep
Learning?[http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf](http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf)
[2] Multilayer feedforward networks are universal approximators:
[http://www.sciencedirect.com/science/article/pii/08936080899...](http://www.sciencedirect.com/science/article/pii/0893608089900208)
[3] Deep Learning, Nature.
[http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf](http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf)
[4] Adam: A Method for Stochastic Optimization.
[https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)

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Guyag
I believe it's a version of an old copypasta [0].

[0]
[https://wiki.installgentoo.com/index.php/Interjection](https://wiki.installgentoo.com/index.php/Interjection)

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brandonb
Well, I guess my sincerity detector is officially broken, huh?

