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With deep learning, you start with sample data, deploy the model, and then expose it to the real world. But models that work well on training data often perform poorly on real data.
Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren’t designed to detect it.
Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x.
Deep learning is a hot topic and many companies feel they need to get started or risk getting left behind. Here are the five main steps on setting up a deep learning workflow.
Recently, Shaila Niazi, a third-year doctoral student in Çamsari’s lab, achieved a significant breakthrough in that effort, becoming the first to use probabilistic hardware to train a deep generative ...
H2O.ai, the AI Cloud leader, today announced H2O Hydrogen Torch, a deep learning training engine that makes it easy for companies of any size in any i ...