Posted By Shivniel Gounder

Researchers and Developers Working Together

TensorFlow is an open source software library for numerical computation using data flow graphs. Originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research.

It is general, flexible, portable, easy-to-use, and completely open source. We added all this while improving upon DistBelief’s speed, scalability, and production readiness — in fact, on some benchmarks, TensorFlow is twice as fast as DistBelief (see the whitepaper for details of TensorFlow’s programming model and implementation).

TensorFlow has extensive built-in support for deep learning, but is far more general than that — any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface.

What we’re hoping is that the community adopts this as a good way of expressing machine learning algorithms of lots of different types, and also contributes to building and improving [TensorFlow] in lots of different and interesting ways, They were not developed with open sourcing in mind. They had a lot of tendrils into existing systems at Google and it would have been hard to sever those tendrils,” Dean says. “With TensorFlow, when we started to develop it, we kind of looked at ourselves and said: ‘Hey, maybe we should open source this” says Jeff Dean

This is really interesting,” says Chris Nicholson, who runs a deep learning startup called Skymind. “Google is five to seven years ahead of the rest of the world. If they open source their tools, this can make everybody else better at machine learning

Machine learning is the secret sauce for the products of tomorrow.It no longer makes sense to have separate tools for researchers in machine learning and people who are developing real products says Greg Corrado, Senior research scientist.

A fair bit of the advancement in deep learning in the past three or four years has been helped by these kinds of libraries, which help researchers focus on their models. They don’t have to worry as much about underlying software engineering,” says Jimmy Ba, a PhD student at the University of Toronto who specializes in deep learning, studying under Geoff Hinton.