One thing to note is that this isn't available on production phones yet, because we need a signed driver to run within Android. You should be able to run this on a Dragonboard 820 development board though, using the instructions in the README.
This is all very new though, so apologies in advance for any hiccups getting up and running. My email's petewarden at google.com if you are trying this and hit problems.
The new, still experimental way is to compile your neural net into executable code with their XLA / tfcompile tool, and link that into your app. They are adding more docs on this on the TensorFlow website .
I'm predicting in a decade we'll have offline speech and image recognition running on the phone.
I think they'll develop a hivemind, where mobile adds to the pool. In short Skynet ;)
I wish AMD graphics cards were supported fully. I really think AMD should find a way to work with the Tensor Flow team on this...
Theano, on the other hand, seems to be focused on the optimizations for the single machine, single GPU code. It only recently got the ability to run each function on a different GPU.
TensorFlow has already become the winner from my reading around it so I'm going to continue learning it rather than another framework until I've become fairly proficient. By which time why change?
If you're looking for something that would make it easier for you to learn DL, you should try Keras - it's a higher level library, which can use both Theano and TF as a backend.
There are quite a few breaking changes but there is a very helpful conversion script here: https://github.com/tensorflow/tensorflow/tree/r1.0/tensorflo....
You can find the breaking changes in the 1.0 release here: https://github.com/tensorflow/tensorflow/releases/tag/v1.0.0
Uh, nope, that was speedup on 64 GPUs (or CPU cores, can't remember). i.e. it scales linearly, something that TF hasn't always been good at v other frameworks. I'm amazed a journalist with (I assume) basic technical competence could make this mistake.
You can follow the Summit live here: https://www.youtube.com/watch?v=LqLyrl-agOw
I have a couple of applications in mind, mostly time series predictions. But the machine learning field seems to be vast and I don't know where to start.
The ML/DNN rabbit-hole goes deep. If the video above leaves you wanting more, http://www.deeplearningbook.org/ does a good job on drilling into more specifics for the various techniques used. The examples on the tensorflow webpage are also very good.
Edit: I should mention that the class mainly focuses on neural networks and image recognition. However, once you have the foundation, you can apply your skillset to a vast range of applications.
Definitely recommend that as a good starting point. Isbell and Littman can be a bit cheesy at points, but they're very clear and thorough.
Don't worry that just because it isn't using deep nets that it isn't state of the art or won't get the job done well. That would be like thinking python's built-in sort function isn't sufficient because it doesn't use Spark.
Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multi-layered neural networks trained on big data. The course focuses particularly on computer vision and language modelling, which are perhaps two of the most recognizable and impressive applications of the deep learning theory.
To be released in June.
The short answer is no.
The long answer is yes, but only if you create the model in Python, export it, and then feed training data in other languages. There are some people doing exactly that.
Long term, I'd like to give all languages equal footing, but there's quite a bit of work left.
Does Python have intrinsic qualities that other languages don't possess or is it that the huge initial investment in creating TensorFlow was based on Python and duplicating that effort somewhere else would require too much work?
I really like Python, but F# <3