tag: recurrent neural networks

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Deep Learning Spreads
By Ed Sperling - 31 Jan, 2018 - Comments: 0

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a convers... » read more

New Materials For Computing
By Ed Sperling - 12 Oct, 2017 - Comments: 0

The U.S. Department of Energy rolled out a new program to develop materials for "extreme conditions" for high-performance computing, setting the stage for much more mobile versions of AI and machine learning. This effort, if successful, has interesting implications on a number of levels. For one, the DOE's mandate includes everything from energy security to weaponry, and high-performance com... » read more

Using CNNs To Speed Up Systems
By Ann Mutschler - 10 Aug, 2017 - Comments: 0

Convolutional neural networks (CNNs) are becoming one of the key differentiators in system performance, reversing a decades-old trend that equated speed with processor clock frequencies, the number of transistors, and the instruction set architecture. Even with today's smartphones and PCs, it's difficult for users to differentiate between processors with 6, 8 or 16 cores. But as the amount o... » read more

Speeding Up Neural Networks
By Ed Sperling - 27 Apr, 2017 - Comments: 2

Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

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