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I tried going through the site and also the comments but couldn't wrap my head around what this library actually is. It sounds awesome based on the response/com

by an4rchy 11y ago

I tried going through the site and also the comments but couldn't wrap my head around what this library actually is. It sounds awesome based on the response/comments. Can anyone explain this to a layman?

gyom 11y ago
If you want to train neural nets, you can either rewrite everything from scratch and get a bug-ridden sub-optimal implementation, or you can use a kind of off-the-shelf library. The problem is that there are about 3-5 alternatives out there, and none of them are mature enough or convincing enough to dominate. The field changes so fast that it's easy for them to become obsolete. What you're seeing here is the enthusiasm of people who really want to get a good tool with proper support, and be able to stick with it. I'm still not sure if TensorFlow is that tool, but it depends on what will happen to it during the coming years.
sputknick 11y ago
It looks like to me the big win here is that this is a ML library just like all the others, except that the code is not written for a specific type of processor (GPU versus CPU), nor is it written for a specific compute level (desktop, server, phone). It's target is to be a general purpose ML library. This should have the affect of getting ML solutions into useable applications more quickly, since you don't have to develop on one platform, then port to another. It just works everywhere. Best guess on my part based on what I'm reading on the site.
levesque 11y ago
It is a library for the optimization of machine learning algorithms, similar to Theano. You write a model you want to optimize as a graph of symbolic expressions, and the library will compile this graph to be executed on a target platform (could be CPU, GPU). This is really neat because you essentially wrote your program in Python and now it's going to be running as optimized C++ code (or as optimized kernels on your GPU). The fact that you defined your model as a symbolic graph means you can derive gradient equations automatically, hence ridding you of a rather tedious step in optimization. With such tools, the workflow for a practitioner is much simpler: 1-code models as graphs of symbolic expressions, and 2-define objective functions (ex. for a regression problem, your objective would be the root mean squared error, plus some regularization). Disclaimer: I did not look at the code, but from what I understand it is pretty much the same as Theano (which I've been using a lot lately).
jheriko 11y ago
it enables you to do lots of maths in parallel in a pretty scalable way.
AdamGS 11y ago
I'm not a machine learning/math guy by any means - but the whitepaper (http://download.tensorflow.org/paper/whitepaper2015.pdf http://download.tensorflow.org/paper/whitepaper2015.pdf) is pretty good and offers some very interesting concepts and comparisons.

by an4rchy 11y ago

gyom 11y ago

If you want to train neural nets, you can either rewrite everything from scratch and get a bug-ridden sub-optimal implementation, or you can use a kind of off-the-shelf library. The problem is that there are about 3-5 alternatives out there, and none of them are mature enough or convincing enough to dominate. The field changes so fast that it's easy for them to become obsolete. What you're seeing here is the enthusiasm of people who really want to get a good tool with proper support, and be able to stick with it. I'm still not sure if TensorFlow is that tool, but it depends on what will happen to it during the coming years.

sputknick 11y ago

It looks like to me the big win here is that this is a ML library just like all the others, except that the code is not written for a specific type of processor (GPU versus CPU), nor is it written for a specific compute level (desktop, server, phone). It's target is to be a general purpose ML library. This should have the affect of getting ML solutions into useable applications more quickly, since you don't have to develop on one platform, then port to another. It just works everywhere. Best guess on my part based on what I'm reading on the site.

levesque 11y ago

It is a library for the optimization of machine learning algorithms, similar to Theano. You write a model you want to optimize as a graph of symbolic expressions, and the library will compile this graph to be executed on a target platform (could be CPU, GPU). This is really neat because you essentially wrote your program in Python and now it's going to be running as optimized C++ code (or as optimized kernels on your GPU). The fact that you defined your model as a symbolic graph means you can derive gradient equations automatically, hence ridding you of a rather tedious step in optimization. With such tools, the workflow for a practitioner is much simpler: 1-code models as graphs of symbolic expressions, and 2-define objective functions (ex. for a regression problem, your objective would be the root mean squared error, plus some regularization). Disclaimer: I did not look at the code, but from what I understand it is pretty much the same as Theano (which I've been using a lot lately).

jheriko 11y ago

it enables you to do lots of maths in parallel in a pretty scalable way.

AdamGS 11y ago

I'm not a machine learning/math guy by any means - but the whitepaper (http://download.tensorflow.org/paper/whitepaper2015.pdf http://download.tensorflow.org/paper/whitepaper2015.pdf) is pretty good and offers some very interesting concepts and comparisons.