Google is announcing TensorFlow, its open source platform for machine learning, giving anyone a computer and internet connection (and casual background in deep learning algorithms) access to one of the most powerful machine learning platforms ever created. More than 50 Google products have adopted TensorFlow to harness deep learning (machine learning using deep neural networks) as a tool, from identifying you and your friends in the Photos app to refining its core search engine. Google has become a machine learning company. Now they’re taking what makes their services special, and giving it to the world.
TensorFlow is a library of files that allows researchers and computer scientists to build systems that break down data, like photos or voice recordings, and have the computer make future decisions based on that information. This is the basis of machine learning: computers understanding data, and then using it to make decisions. When scaled to be very complex, machine learning is a stab at making computers smarter. That's the broader, and more ill-defined field of artificial intelligence. TensorFlow is extraordinary complex, because of its precision and speedin digesting and outputting data, and can unequivocally be placed in the realm of artificial intelligence tools.
The TensorFlow system uses data flow graphs. In this system, data with multiple dimensions (values) are passed along from mathematical computation to mathematical computation. Those complex bits of data are called tensors. The math-y bits are called nodes, and the way the data changes from node to node tells the overall system relationships in the data. These tensors flow through the graph of nodes, and that's where the name TensorFlow comes from.
Open-sourcing TensorFlow allows researchers and even grad students the opportunity to work with professionally-built software, sure, but the real effect is the potential to inform every machine learning company’s research across the board. Now organizations of all sizes—from small startups to huge companies on par with Google—can take the TensorFlow system, adapt it to their own needs, and use it to compete directly against Google itself. More than anything, the release gives the world’s largest internet company authority in artificial intelligence.
http://www.tensorflow.org/
Google's internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.
While DistBelief was very successful, it had some limitations. It was narrowly targeted to neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure -- making it nearly impossible to share research code externally.
TensorFlow is their second-generation machine learning system, specifically designed to correct these shortcomings. TensorFlow is general, flexible, portable, easy-to-use, and completely open source. They 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.
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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TensorFlow is a library of files that allows researchers and computer scientists to build systems that break down data, like photos or voice recordings, and have the computer make future decisions based on that information. This is the basis of machine learning: computers understanding data, and then using it to make decisions. When scaled to be very complex, machine learning is a stab at making computers smarter. That's the broader, and more ill-defined field of artificial intelligence. TensorFlow is extraordinary complex, because of its precision and speedin digesting and outputting data, and can unequivocally be placed in the realm of artificial intelligence tools.
The TensorFlow system uses data flow graphs. In this system, data with multiple dimensions (values) are passed along from mathematical computation to mathematical computation. Those complex bits of data are called tensors. The math-y bits are called nodes, and the way the data changes from node to node tells the overall system relationships in the data. These tensors flow through the graph of nodes, and that's where the name TensorFlow comes from.
Open-sourcing TensorFlow allows researchers and even grad students the opportunity to work with professionally-built software, sure, but the real effect is the potential to inform every machine learning company’s research across the board. Now organizations of all sizes—from small startups to huge companies on par with Google—can take the TensorFlow system, adapt it to their own needs, and use it to compete directly against Google itself. More than anything, the release gives the world’s largest internet company authority in artificial intelligence.
http://www.tensorflow.org/
Google's internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.
While DistBelief was very successful, it had some limitations. It was narrowly targeted to neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure -- making it nearly impossible to share research code externally.
TensorFlow is their second-generation machine learning system, specifically designed to correct these shortcomings. TensorFlow is general, flexible, portable, easy-to-use, and completely open source. They 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.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Read more »
