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When building predictive models, the information provided to the model and the manner in which it is encoded or formatted is known as the input representation. The choice of representation can determine what can be learned and how readily it will be learned. Read More
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Pictures of AnswerOn’s staff volunteering with Habitat for Humanity of St. Vrain Valley! Read More
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Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses issues facing big data and machine learning. What does “big data” mean and how can it help us with analysis? Read More
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Although neural networks have been around for sixty years, a seminal research paper appeared thirty years ago that revolutionized the field. The paper was called Learning representations by back-propagating errors (Rumelhart, Hinton, & Williams, 1986). In this post, I’ll explain what “learning representations” means and why this idea is so central to neural networks. Read More
Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses the rise and fall, and rise again of neural networks. This post considers how we owe today’s neural networks to those from the 1980s. Read More