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Adaptive gradient algorithm used for large scale machine learning tasks in distributed environments.

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In 2012, Google X lab successfully used Adagrad to train a neural network of 16,000 computer processors and 1 billion connections to browse random YouTube videos and recognize high-level features. Over the course of a three day trial, the system achieved 81.7% accuracy in detecting human faces, 76.7% accuracy when identifying human body parts, and 74.8% accuracy when identifying cats. This was unsupervised learning, meaning that the algorithm wasn't fed any information to help it identify distinguishing features and wasn't viewing pre-labled images or videos.

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