Google has released the latest version of TensorFlow, their open-source machine learning package at their recent Google I/O event. TensorFlow runs on Android, iOS, Raspberry Pi and both Google and AWS cloud services, using the same API. TensorFlow supports a wide range of CPUs, GPUs., and now TPUs (Tensor Processing Units). Tensor is Google's name for vectors and matrices. Google provides TensorFlow API support in four languages: Python, C++, Java, & Go. Other groups have ported the TensorFlow API to Haskell, Julia, C#, and R. TensorFlow is designed to hide the concurrent nature of the underlying processes, so large numbers of parallel processes can be treated as a single process at the top-level TensorFlow API.
Check out the TensorFlow overview video given at Google I/O last week (36 minutes) on YouTube at: https://www.youtube.com/watch?v=OzAdKMPgUt4&list=TLGGqXCgIcW-mFUyNDA1MjAxNw Check out the basic TensorFlow machine learning formula at 18:36 in the video. This works as a line of J code (getting rid of the square brackets, and adding parenthesis to override J's right-to-left execution). Imagine! You can debug a machine-learning application on one's PC or smartphone using a test dataset. Then you run the compute-intensive big-data training in the cloud, running on dozens (or hundreds) of GPUs, or now TPUs. Finally, one can run the trained models back on a local machine, or keep the model execution in the cloud, if the model still needs lots of processing. This seems like a perfect fit for a J TensorFlow library. Unfortunately, implementation of such a library is way above my J skill level. Skip Cave Cave Consulting LLC ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
