Google recently open sourced TensorFlow providing access to a powerful machine learning system. TensorFlow is a machine learning library with tools for data scientists to design intelligent systems (interface for expressing machine learning algorithms and implementation for executing such algorithms). It runs on CPUs or GPUs, and on desktop, server, laptop, or mobile platforms with a single API. See paper here.
Originally developed by the Google Brain Team in Machine Intelligence Research, TensorFlow has a flexible, portable and general architecture for a wide variety of applications. The system has been used for deploying machine learning systems for information retrieval, simulations, speech recognition, computer vision, robotics, natural language processing, geographic information extraction, and computational drug discovery.
The system uses data flow graphs where data with multiple dimensions (values) are passed along from mathematical computation to mathematical computation. Complex bits of data are tensors and math-y bits are nodes, and tensors flow through the graph of nodes. The way data transforms from node to node tells the system relationships in the data.
What is really cool is the systems flexibility and simplicity with ability to quickly experiment with different ideas on the laptop, easily move into production, use GPUs with no code changes, and deploy on a mobile device.
The ability to simply and easily deploy products on mobile devices is a valuable feature. It can be used on a wide variety of heterogeneous systems, including large-scale distributed systems.
TensorFlow has an Apache 2.0 open source license for use commercially.
See: http://bit.ly/1ow9EVF
Comment
Tensor calculus & multi-linear algebra has come a long way when it was first published in the 1900. So, tensor mathematics is more than 100 years now. Albert Einstein applied tensor calculus to develop his theory of general relativity in 1916. The linear algebra community revived the idea of tensor in the 1960s with the development of Higher-order SVD (singular value decomposition) which is multi-dimensional or multi-mode SVD. Over the last 12 years or so, seen the Machine Learning & Statistics communities picked up on tensors and research work in this area is increasing because one can tell by the number of publications that publishes on data analytics that use tensors.
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