If there is one subset of machine learning that spurs the most excitement, that seems most like the intelligence in artificial intelligence, it’s deep learning.

Deep learning frameworks—aka deep neural networks—power complex pattern-recognition systems that provide everything from automated language translation to image identification.Deep learning holds enormous promise for analyzing unstructured data.

There are just three problems: It’s hard to do, it requires large amounts of data, and it uses lots of processing power. Naturally, great minds are at work to overcome these challenges.  [ Also on InfoWorld: What deep learning really means | Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Cut to the key news and issues in cutting-edge enterprise technology with the InfoWorld Daily newsletter. ]
What’s now brewing in this space isn’t just a clash of supremacy between competing deep learning frameworks, such as Google’s TensorFlow versus projects like Baidu’s Paddle. Rivalry between multiple software frameworks is a given in most any part of IT.To read this article in full or to leave a comment, please click here

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