Predicting Time Series for the Internet of Things
Deep learning has been a hot topic for the past several years, but most of the showcase applications for deep learning have centered on natural language processing and image classification. What are the advantages and disadvantages of applying deep learning algorithms to sensor-based data? In this talk we will focus on recurrent neural networks and their use on time series-based sensor data.
Sean Lorenz is Founder & CEO of Senter, a startup improving chronic care with Internet of Things (IoT) and deep learning in the home. He has shaped business models and product strategies in several emerging markets including the IoT, robotics, artificial intelligence and healthcare. Sean holds a PhD in Cognitive & Neural Systems from Boston University and has extensive knowledge of digital health, natural language processing, brain-computer interfaces, adaptive systems, neuroscience-based computational algorithms, and context-aware computing.
Government, Enterprise, Small / Medium Enterprise, OEM
Predictive Analytics, Machine Learning, IoT, time series, sensor data, deep learning
CxO, VP / Director, Technical
Retail, Manufacturing, Industrials, Healthcare, Consumer, Government / Public Sector, Automotive
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