Can someone please spell-out for us muggles what sets these frameworks (Theano, Tensorflow, Torch, CNTK, Mxnet) apart ? They all seem to be essentially doing the same thing underneath.
MXNet – Deep Learning Framework of Choice at AWS
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Re: MXNet – Deep Learning Framework of Choice at AWS
#52Can someone please spell-out for us muggles what sets these frameworks (Theano, Tensorflow, Torch, CNTK, Mxnet) apart ? They all seem to be essentially doing the same thing underneath.
Cloud vendor feature signaling, mostly.
Microsoft wants you to use CNTK on Azure. Amazon wants you to use Mxnet on AWS. Google wants you to use Tensorflow on GCP.
It's irrelevant whether these frameworks can be used outside their home platform by broke college students. That's a red herring. The cloud vendors are looking to sell enterprise contracts, and they need to check all of the boxes.
This strategy makes complete sense from a business perspective, and you really cannot fault them for doing it.
Re: MXNet – Deep Learning Framework of Choice at AWS
#53> Machine learning (...) is being employed in a range of computing tasks where programming explicit algorithms is infeasible. I found this comment interesting. Is this really the summary of what machine learning is about?
Image classification is a classic example of such a task. How exactly would you go about writing an algorithm to tell the difference between a picture of a cat and a picture of a dog?
Well, this might be cheating, but I would apply a bunch of different filters for things like edge-detection, etc. Then I would come up with a statistical model that, for each feature, gave the likelihood that there image under consideration was a dog. Then I would aggregate all those results into a final likelihood.
Not trying to be sarcastic, I just can't think of any way other than the ML way.
Re: MXNet – Deep Learning Framework of Choice at AWS
#54Earlier quoted context omitted.
Image classification is a classic example of such a task. How exactly would you go about writing an algorithm to tell the difference between a picture of a cat and a picture of a dog?
Well, this might be cheating, but I would apply a bunch of different filters for things like edge-detection, etc. Then I would come up with a statistical model that, for each feature, gave the likelihood that there image under consideration was a dog. Then I would aggregate all those results into a final likelihood. Not trying to be sarcastic, I just can't think of any way other than the ML way.
How to draw an owl: http://imgur.com/gallery/RadSf
Re: MXNet – Deep Learning Framework of Choice at AWS
#55Earlier quoted context omitted.
Image classification is a classic example of such a task. How exactly would you go about writing an algorithm to tell the difference between a picture of a cat and a picture of a dog?
Well, this might be cheating, but I would apply a bunch of different filters for things like edge-detection, etc. Then I would come up with a statistical model that, for each feature, gave the likelihood that there image under consideration was a dog. Then I would aggregate all those results into a final likelihood. Not trying to be sarcastic, I just can't think of any way other than the ML way.
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Re: MXNet – Deep Learning Framework of Choice at AWS
#56Earlier quoted context omitted.
Image classification is a classic example of such a task. How exactly would you go about writing an algorithm to tell the difference between a picture of a cat and a picture of a dog?
Well, this might be cheating, but I would apply a bunch of different filters for things like edge-detection, etc. Then I would come up with a statistical model that, for each feature, gave the likelihood that there image under consideration was a dog. Then I would aggregate all those results into a final likelihood. Not trying to be sarcastic, I just can't think of any way other than the ML way.
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Re: MXNet – Deep Learning Framework of Choice at AWS
#57Translation from corporatespeak: "We don't have an internally developed framework that can compete with TensorFlow, which is controlled by Google, so we are throwing our weight behind MXNet." As others have commented here, there is no evidence that MXNet is that much better (or worse) than the other frameworks.
Amazon has been building technology based on ML&DL for over 20 years and has developed several frameworks. You must have missed the announcement of this open source framework earlier in the year: https://github.com/amznlabs/amazon-dsstne .
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Re: MXNet – Deep Learning Framework of Choice at AWS
#58Translation from corporatespeak: "We don't have an internally developed framework that can compete with TensorFlow, which is controlled by Google, so we are throwing our weight behind MXNet." As others have commented here, there is no evidence that MXNet is that much better (or worse) than the other frameworks.
Amazon has been building technology based on ML&DL for over 20 years and has developed several frameworks. You must have missed the announcement of this open source framework earlier in the year: https://github.com/amznlabs/amazon-dsstne .
Here is a very nice blog article explaining how Amazon is generating recommendations at scale with Apache Spark and Amazon DSSTNE :) https://aws.amazon.com/blogs/big-data/generating-recommendat...
Re: MXNet – Deep Learning Framework of Choice at AWS
#59Earlier quoted context omitted.
Image classification is a classic example of such a task. How exactly would you go about writing an algorithm to tell the difference between a picture of a cat and a picture of a dog?
Well, this might be cheating, but I would apply a bunch of different filters for things like edge-detection, etc. Then I would come up with a statistical model that, for each feature, gave the likelihood that there image under consideration was a dog. Then I would aggregate all those results into a final likelihood. Not trying to be sarcastic, I just can't think of any way other than the ML way.
To further the point: what filters would you choose? What features could you choose heuristically to distinguish between the two? They both have fur, they both have four legs, they both have two eyes, they both come in a wide variety of colors and patterns... Most dogs have an elongated snout but not all of them (pugs, bulldog, etc.).
I would be extremely impressed if someone developed an algorithm that could accomplish this task without using any type of statistical/machine learning.
Re: MXNet – Deep Learning Framework of Choice at AWS
#60Translation from corporatespeak: "We don't have an internally developed framework that can compete with TensorFlow, which is controlled by Google, so we are throwing our weight behind MXNet." As others have commented here, there is no evidence that MXNet is that much better (or worse) than the other frameworks.
Amazon has been building technology based on ML&DL for over 20 years and has developed several frameworks. You must have missed the announcement of this open source framework earlier in the year: https://github.com/amznlabs/amazon-dsstne .
This doesn't address the root comment at all. Does Amazon actually think MXNet is the best? Or did they simply choose the next best thing that isn't already backed by another "big four" company (Google -> TensorFlow, Facebook -> Torch). It's hard to believe this is actually about scalability without any data.